>> seismic interpretation here

Strong disagree here. Lets put aside the math and focus on money.

I dont know much about seismic interpretation, but I know a lot about Radiology+CV/ML. I was CTO+CoFounder for three years full time of a venture-backed Radiology+CV/ML startup.

From what I can see, there is a huge conflict of interest w/r/t Radiology (and presumably any medical field) in the US. Radiologists make a lot of money -- and given their jobs are not tied to high CoL regions (as coders jobs are), they make even more on a CoL-adjust basis. Automating these jobs is the equivalent of killing the golden goose.

Further, Radiologists standards of practice are driven partly by their board (The American Board of Radiology) and the supply of labor is also controlled by them (The American Board of Radiology) by way of limited residency spots to train new radiologists.

So Radiologists (or any medical specialist) can essentially control the supply of labor, and control the standards of best practice, essentially allowing continued high salaries by way of artificial scarcity. WHY ON EARTH WOULD THEY WANT THEIR WORK AUTOMATED AWAY?

My experience during my startup was lots of radiologists mildly interested in CV/ML/AI, interested in lots of discussions, interested in paid advisory roles, interested in paid CMO figurehead-positions, but mostly dragging their feet and hindering real progress, presumably because of the threat it posed. Every action item was hindered by a variety of players in the ecosystem.

In fact, most of our R&D and testing was done overseas in a more friendly single payer system. I dont see how the US's fee-for-service model for Radiology is ever compatible with real progress to drive down costs or drive up volume/value.

Not surprisingly, we made a decision to mostly move on. You can see Enlitic (a competitor) didnt do well either despite the star-studded executive team. Another competitor (to be unnamed) appears to have shifted from models to just licensing data. Same for IBM/Merge.

Going back to seismic interpretation -- this cant be compared to Radiology from a follow-the-money perspective because seismic interpretation isnt effectively a cartel.

Happy to speak offline if anyone is curious about specific experiences. DM me.

I hear this sort of argument a lot in different fields. Usually it's because the IT guy doesn't really understand the business they are trying to automate or where the true pinch points or time savings are.

Could you provide some examples of fields where practitioners control both supply and standard of practice where automation is also shunned, perpetuating high costs? Also, note, the largest source of bankruptcy in the US is medical costs https://www.cnbc.com/2019/02/11/this-is-the-real-reason-most...

"They dont understand the business" is a great excuse for maintaining status quo. I'm an Engineer, a quant, and a computer scientist by training and I refuse to accept defeat w/o sound reason. I will if I'm given a good reason, but "go away you guys, you dont understand our business" is defeatist. If we all accepted such answers society would never progress. I'm sure horse carriages said the same thing when people tried to invent motor vehicles.

So first of all, you’re incorrect about medical costs being the number one reason for bankruptcies: https://www.washingtonpost.com/politics/2019/08/28/sanderss-...

I’ll give you a concrete example in the legal field. Big firms might have reasons to avoid labor-saving automation, because they bill by the hour. But a large fraction of legal work isn’t billed by the hour, it’s contingency work (where the firm gets a certain fraction of a recovery) or fixed fee work. If you’re getting paid 1/3 of the amount you recover (a typical contingency fee) you have enormous incentives to do as little work to get a good result as you can. But those firms don’t use a lot of legal technology either, because it’s just not very good and not very useful.

The bulk of legal practice is about dealing with case-specific facts and legal wrinkles. And machine learning tends not to be useful for that, at least in current forms.

So, machine learning does get used quite a bit in the legal industry, at least outside of small practice. But it tends to be much more successful when it's used as a force multiplier for humans rather than a replacement for humans.

For example, the idea of using document classification to reduce review costs has been around for a long time. But it took a long time to get any traction. Some of that was about familiarity, but a lot of it was about the original systems being designed to solve the wrong problem. The first products were designed to treat the job as a fairly straightforward binary classification problem. They generally accomplished that task very well. The problem was you had to have a serious case of techie tunnel vision to ever think that legal document classification was just a straightforward binary classification problem in the first place.

Nowadays there are newer versions of the technology that were designed by people with a more intimate understanding of the full business context of large-scale litigation, and consequently are solving a radically reframed version of the problem. They are seeing much more traction.

The coordination problems in creating a system designed from the beginning to be human in the loop is a challenge.

There are a lot of great ML algorithms, even if you limit yourself to 10-20 year old ones, that aren't leveraged anywhere like how they could be because very few know how to build such a system by turning business problems into ML problems and training users to work effectively alongside the algorithm.

CRUD application development projects blow past deadlines and budgets frequently enough. ML projects have even greater risks.

Edit: I hope the people making the successful legal document management system you mentioned write about their experience.

FWIW, my experience has been that, if you're trying to build a system that works in tight coordination with humans, you're better off sticking to algorithms that are 40-80 years old. Save some energy for dealing with the part that's actually hard.

That WP article doesn’t support your claim. It’s about the number of bankruptcies, not the leading cause. Nonetheless it does cite a survey that found medical bills contributed to 60+% of bankruptcies, and that it doesn’t really make sense to talk about a single cause.

It's a stat that requires a lot of contextualization. To your point, you're absolutely correct that the number of bankruptcies is important, because over the last couple decades, 1) bankruptcies in general have been falling, 2) medical bankruptcies have also been falling in absolute terms; but because the denominator has dramatically fallen relative to the numerator, the numerator looks larger than it actually is.

https://www.theatlantic.com/business/archive/2009/06/elizabe...

In other words, medical bankruptcies have fallen in absolute terms, but you wouldn't know that by just looking at the %age of bankruptcies.

Why not simplify the medical bankruptcy discussion?

Fact is Americans have high personal cost and risk exposure relative to nearly all of the rest of the world.

Second, our system has making money as the priority, again in contrast to much of the world.

Finally, most of the world recognizes the inherent conflict of interest between for profit and sick/hurt people and both regulate that conflict to marginalize it, and make it so people have options that make sense.

My take, having been chewed up by our toxic healthcare system twice now (having a family does matter, lol), is the temporary dampening on cost and risk escalation starting the ACA brought to us is fading now, and issues are exceberated by the pandemic (demand for care crashing into variable supply), and shifted somewhat as large numbers of people fall into subsidy medicaid type programs due to job loss.

The honeymoon period is long over now, and the drive to "make the number" is going to be front and center and escalating from here.

TL;DR: We are not improving on this front at all. We need to.

I could go on at length about high student debt and it's impact on these discussions too.

The radiology control over labor, preserving income for it's members is totally real, and fron their point of view, necessary. They ask the legitimate question in the US: How can I afford to practice.

Most of the world does not put their medical people in positions to ask that question, with some exceptions, those being far more rare and easily discussed than most of the topic is here.

Full disclosure: I work in healthcare pricing, so I have some first hand insight into all of this

> Fact is Americans have high personal cost and risk exposure relative to nearly all of the rest of the world.

This is only true for some Americans, and increasingly very few. I actually found this tweet by a health policy expert to perfectly capture the status quo: https://twitter.com/CPopeHC/status/1234510323425652737

"American healthcare in short: ~60% (in good employer plans, generous state Medicaid, or M.Adv/Medigap) have the best healthcare in the world. ~30% have insurance with gaps/risk of big bills. ~10% uninsured must rely on uncompensated care, go without treatment, or risk bankruptcy

The strength of M4A proposals is that they begin with an understanding that the 40% exist and need things fixed. Their weakness is that they pretend that the 60% don't, and threaten to take away what they have."

The fact of the matter is that the majority of Americans have excellent, world class health coverage. The problem is that there exists a small percentage of Americans that are totally screwed, and this is a higher percentage than most other comparable countries. There are a couple reasons why, which brings me to...

> Second, our system has making money as the priority, again in contrast to much of the world.

First of all, this is false insofar as not all health insurance in America is for-profit. Blue Cross Blue Shield, for example, are predominately 501 non-profits (with a few notable exceptions).

Second of all, while you're right that much of the world has public insurance companies that don't seek to "make money", there are a number of countries with world class healthcare that do have profit seeking insurance, many of them with purely private profit driven insurance companies: including Switzerland and the Netherlands. Some have a hybrid of public/private, including Germany (public/private mix), Singapore (public/private mix), etc. In fact, while many countries have a public insurance system, it is extraordinarily rare for countries to outright ban private insurance options.

Third of all, in America, health insurance is one of the most regulated industries in the country. After ACA was passed, there's a strict cap on profit margins that health insurers can enjoy. It's not too dissimilar from how private health insurance is regulated in Switzerland and the Netherlands, both of which have some of the best healthcare on the planet.

> Finally, most of the world recognizes the inherent conflict of interest between for profit and sick/hurt people and both regulate that conflict to marginalize it, and make it so people have options that make sense.

Again, as I mentioned above, this is not only not true, it's debatable if such an "inherent" conflict of interest even exists. By this logic, there should be an inherent conflict of interest between for profit food providers and "hungry/starving" people. The profit motive alone can't explain America's health outcomes, because there exists countries with fantastic healthcare systems (Switzerland, Netherlands) which are driven purely by private health insurance.

America actually has a pretty good apples-to-apples experiment of "profit seeking" vs "not profit seeking" insurance, ironically in Medicare Advantage. When you turn 65, you have the option to enroll either in "Original Medicare", which is what we usually think of when we talk about "single payer healthcare in America", or you can enroll in Medicare Advantage (aka Medicare "Part C"), where the premiums that would go to the CMS instead go to private insurers like Humana, United, Oscar Health, Aetna, Clover, etc. These plans replace Original Medicare, also cover Part D prescription drug benefits, and often include supplemental benefits that Original Medicare doesn't already cover. There are some interesting findings so far:

- 39% of Medicare beneficiaries are on private Medicare Advantage plans instead of the public "Original Medicare". Because everyone is entitled to "Original Medicare", this is purely voluntary. This number has been growing so rapidly, that we expect by 2025, more seniors to be on a private plan than the public one. There's also great variance by State. In Florida, Pennsylvania, Wisconsin, Michigan, Minnesota, Oregon, Alabama, Hawaii, and Connecticut — nearly 50% of beneficiaries are on Medicare Advantage. By 2022, we expect more seniors in those States to be on a private plan than a public one. https://www.kff.org/medicare/issue-brief/a-dozen-facts-about...

- For most beneficiaries, Medicare Advantage costs about 39% less than Original Medicare. https://www.kff.org/medicare/issue-brief/a-dozen-facts-about...

- Medicare Advantage plans are, on average, of higher quality than the public Original Medicare. https://healthpayerintelligence.com/news/medicare-advantage-...

- In Urban areas, Medicare Advantage costs less per capita to administer than Medicare — and that's not including the extra Medicare Part D insurance that you would have to buy if you're on the Original Medicare plan. https://www.commonwealthfund.org/publications/issue-briefs/2...

So the reality is really more complicated than you're making it out to be.

From where I sit, the one thing that sets apart America from the rest of the world is not that health insurance can be profit driven (so do the Swiss and the Dutch, for example), it's that health insurance is coupled with employment. There's really no other peer nation for which this is the case, and a lot of the economics of health insurance look the way that they do because big employers buy most of the health insurance in today's market, and that has resulted in market distortions that hurt those that are unemployed. What we're seeing in healthcare costs is analogous to what you might see happen to airline ticket costs if we all got our air tickets through our employers: the vast majority of us would fly business class, while the unemployed would be simply unable to pay for business class fares out of pocket. Employers (especially medium-to-large businesses) have a much higher purchasing power (and hence, willingness to pay) than individuals.

> The fact of the matter is that the majority of Americans have excellent, world class health coverage. The problem is that there exists a small percentage of Americans that are totally screwed, and this is a higher percentage than most other comparable countries. There are a couple reasons why, which brings me to...

Really? That is news to me, as a dual US|EU (Croatian) citizen, who is culturally American--but currently living in Croatia.

Even Croatia has a higher life expectancy than the United States. Yep, even those "eastern European countries" (that are within the European Union) that Americans refer to with derision, often have higher life expectancy than the United States.

See: https://www.reddit.com/r/croatia/comments/nuiyk1/hrvatska_sp...

Also, just in case you want to blame this on "lifestyle factors" (which means that this is a public health matter, which the United States has severely underfunded--locally, state, and nationally for more than a few decades now), the third leading cause of death is believed to be preventable medical errors. (The source I provide has been verified by several follow-up studies.)

See: https://www.bmj.com/content/353/bmj.i2139/rapid-responses

Also, you don't know what you are talking about here. I have studied healthcare systems worldwide for hundreds of hours.

Ironically the IMHE group that does the coronavirus projections is world renowned at producing this data. All of this data is open-access.

For starters (easy reading) this article is relevant: http://www.healthdata.org/news-release/how-healthy-will-we-b...

But, seriously, we have far from the best healthcare system in the world. That is not even remotely true. There are several countries where a woman can give birth and is less likely to die, compared to the US.

I wish you were joking.

> Really? That is news to me, as a dual US|EU (Croatian) citizen, who is culturally American--but currently living in Croatia.

Yes, and just like that health policy analyst, I can attest to it. I've read more than enough plan documents, and work with health actuaries every day.

> Also, just in case you want to blame this on "lifestyle factors" (which means that this is a public health matter, which the United States has severely underfunded--locally, state, and nationally for more than a few decades now), the third leading cause of death is believed to be preventable medical errors. (The source I provide has been verified by several follow-up studies.)

Actually, there's a fantastic analysis that addresses this point head on, even analyzing the IHME data: https://randomcriticalanalysis.com/2017/05/16/the-explanator...

The vast majority of the variance in average life expectancy is attributable to lifestyle factors. As long as you stay away from drugs, you don't participate in a gang, or you take the bus (or any public transit), you're on roughly equal footing with the rest of the OECD.

"The data suggests motor vehicle accidents, homicides, and drug overdose deaths can explain a large fraction of the US life expectancy gap as compared to several highly developed countries. Obviously this does not account for obesity, diabetes, (historical) smoking, and related lifestyle differences that are likely to have a pronounced negative affects on US life expectancy as compared to most other developed countries and which statistically explains the vast majority of the very large spatial differences in the United States."

> Also, you don't know what you are talking about here. I have studied healthcare systems worldwide for hundreds of hours.

Um, so have I. I literally work on health pricing systems, and have studied health policy. "For hundreds of hours" even, for whatever that's worth (not a lot, I assure you).

> But, seriously, we have far from the best healthcare system in the world. That is not even remotely true. There are several countries where a woman can give birth and is less likely to die, compared to the US.

I don't think I ever said that we have the best healthcare in the world. I agree that US healthcare is broken. All I'm pointing out to you is that the "profit motive" has nothing to do with that, as evidenced by counterfactuals in Switerland, Singapore, and the Netherlands; the former two of which actually have the best healthcare in the world.

https://www.rd.com/article/switzerland-worlds-best-healthcar...

https://www.forbes.com/sites/theapothecary/2011/04/29/why-sw...

https://www.bloomberg.com/graphics/infographics/most-efficie...

In my opinion, the profit motive has nothing to do with America's healthcare ills (no pun intended). It's the fact that it's tied to employment and purchased by employers. No other country is set up that way.

This is the most HN argument I've ever seen.

Personally, I defer to you, the person who actually understands the industry from the inside, in terms of having an opinion based in reality.

So often, these hand-wavy solutions which boil down to "we must remove the bad people preventing our utopia" (ie scapegoating) are masking wicked problems (https://en.m.wikipedia.org/wiki/Wicked_problem) that cross multiple thresholds of responsibility, incentive and jurisdiction.

Declaring hard problems to be caused intentionally by evil people has led to some of the most despicable acts in history.

> I've read more than enough plan documents, and work with health actuaries every day.

So, part of your job is to analyze health benefits plans (health insurance plans) that Americans get. You also work daily with actuaries in the life sector, who assign dollar values to people's lives.

Yeah, like that really makes you a good source when it comes to the well-being and long-term outcomes of a country.

> In my opinion, the profit motive has nothing to do with America's healthcare ills (no pun intended). It's the fact that it's tied to employment and purchased by employers. No other country is set up that way.

Congratulations on coming up with that point. That is precisely why I left the US, as somebody with a rare disease that requires an orphan drug to survive.

I knew better than to stay in the US, in order to survive. In fact, there may be a major ACA Supreme Court decision coming soon. If not, it will be released in the next session. I refresh SCOTUSblog every morning, worrying for my fellow Americans, who could very well die from the outcome of the decision. Regardless, I never plan on living in the US ever again. It will never be "home" for me anymore.

> So, part of your job is to analyze health benefits plans (health insurance plans) that Americans get. You also work daily with actuaries in the life sector, who assign dollar values to people's lives.

Yes, exactly like health insurance actuaries at publicly run health insurance providers. We don't sit around trying to figure out how to make people die, like cartoon villains. We try to figure out how to make healthcare sustainable.

If you read what I had written, it's clear that not only do the private sector insurance providers perform comparable with public sector ones like Original Medicare, they can even out-perform them. So we can't conclude the "privateness" as the root cause of our problems, we have to consider other confounding variables.

> Congratulations on coming up with that point. That is precisely why I left the US, as somebody with a rare disease that requires an orphan drug to survive.

Sorry to hear that, truly. In my opinion, the single most effective thing we can do to help folks like you is to decouple health insurance from employment, and I'm sticking around to try to make that happen. Hopefully you'll come back, and stay healthy.

Thank you. Like another poster suggested, I will try to be more considerate next time.

I am off disability, but I can theoretically keep Medicare for life. I was always on traditional Medicare, and my orphan drug (a blood product) was covered under Part D for my condition. I was also insured as a "disabled dependent" via employer-based insurance, through my deceased father's retiree benefit--so it was secondary insurance--which functioned like a supplemental plan.

I have 2 rare immune-mediated neurological diseases affecting my peripheral nervous system (one of them being very rare--which means an HMO from a Medicare Advantage plan is a huge problem if I want to stay alive long term in the US--and generally, you cannot go back to traditional Medicare), plus type 1 diabetes. The very rare neurological disease is believed to have caused the autoimmunity leading to my diabetes diagnosis at age 5.

Anyways, I can tell you that the way things were set up in the US (prior authorizations, prescription formulary restrictions, quantity limits, networks, etc.) were certainly harming my health. I studied electrical engineering for undergraduate, and it is not like I am cannot handle bureaucratic and logistical nightmares.

But, there is a baseline level of stress and anxiety that is present in the US, and you do not have the realistic expectation that you will be cared for there. Not only that, it is a part-time job just to deal with insurance matters. This feeling is basically non-existent within most of the EU, including in places like Croatia. Croatians probably do have the best lifestyle in all of Europe, too.

It's just not worth it.

> generally, you cannot go back to traditional Medicare

You're always able to go back to traditional Medicare, you just have to wait until the next open enrollment. In fact, after the first trial run with MedAdv, you can switch back before open enrollment if you want. Traditional Medicare is always an option.

Great to hear that you're staying healthy otherwise.

Thank you! :-)

True, but the issue is that medical underwriting is allowed on Medigap (Part B supplemental plans). So, once you are on a Medicare Advantage Plan, you basically cannot effectively go back, due to being unable to obtain a Medigap plan (covering the 20% that part B does not cover), due to having pre-existing condtion(s). The financial consequences of not having a Medigap plan are quite severe for somebody who has a rare disease, if you know what I mean.

As you know, it is a loophole in the ACA. Because I was declared disabled before age 22 ("disabled adult child"), there is a way for me to get Medicaid, for life, effectively, through the Ticket-to-Work program, via the PASS (Plan to achieve self support). Even if I "make to much money to stay on Medicaid", at some point, the Pickle Amendment allows me to stay on it for life, due to the age I was declared disabled at.

But, there are issues with that too, since it is a form of "welfare". You can end up having to pay back the US government hardcore overall. You also get punished for being on Medicaid. For example, some states only allow you to have 4 medications covered by Medicaid. After the 4th active prescription, a type of "prior authorization" is sent to each and every doctor--for some government bureaucrat to make an arbitrary decision whether this medication is being "worthy of coverage".

It feels like this is the point in the thread where you just got frustrated and started casting aspersions instead of making arguments. That happens to me a bunch too, and the strategy I've developed for it is to look at how many question marks I've managed to put in my comments, and try to fit more of them in. The person you're arguing with has some apparent domain knowledge; try extracting it?

This is fine. I will try being more thoughtful next time.

>All I'm pointing out to you is that the "profit motive" has nothing to do with that

You have not met that burden. Not even close.

The best case is a mixed environment, with a for profit portion that can address clear for profit cases well.

And those exist!

But, doing that sans a robust system that actually just delivers health care to sick people is crazy bad policy.

There is another argument in your favor out there, and that is consistent, transparent pricing. Or "Equal Pricing"

In Singapore, there are no real surprises and people have options that don't cause them to trade, homes for example, to get sick people they care about healthy again.

Here? Nothing but surprises!

And frankly, that being the case actually does support the difficult argument:

If making money is the priority, then making sick people healthy isn't.

On the other hand, if making sick people healthy is the top priority, and then we talk about money?

Very different scenarios.

The US is firmly entrenched in the former. Examples of the latter exist in the world and perform well.

The profit motive matters. How it's framed, what priority it has, and more all do contribute to the overall effectiveness and again that cost and risk exposure.

> You have not met that burden. Not even close.

What? I absolutely have. I’m not sure what you’re on about. Switzerland is as close as it gets to a profit-driven purely private healthcare system. Indeed, the US was modeled off of it. The only difference between the two is that the former is based on a robust individual market while the latter is driven by group benefits.

> But doing that sans a robust system that actually just delivers health care to sick people is just bad policy

I don’t think anybody is suggesting not delivering health care to sick people. The question is whether the private sector can provide an actuarial product.

> In Singapore, there are no real surprises

Agree, Singapore’s healthcare system is excellent, and price transparency is very important. The reason the US system is devoid of price transparency is because the majority of Americans simply don’t care about prices, since they have little skin in the game. This is true for old people on Original Medicare, poor people on Medicaid, as well as employed people on generous group plans. If none of those describe you, then you’re unfortunately SOL. THAT’S the problem. Not the profit motive.

> If making money is the priority, then making sick people healthy isn’t

Again, it’s a cute pithy quote, but that’s not how the real world works. If making money is a priority, then feeding hungry people isn’t. If making money is a priority, then providing cheap clothing and shelter isn’t. It’s impossible to understand how the world works through such a simplistic lens.

As I showed you above, the Medicare A/B test is illuminating. Medicare Advantage payers are primarily in the business of making money, and yet their members are on average healthier, have higher quality plans, and at lower cost.

Feeding people isn't a priority!

World hunger anyone! It's a real thing.

We have it because making money is a higher priority than feeding people is.

Cheers, I have other things to do, and did enjoy this discussion!

> Feeding people isn’t a priority!

Feeding people is 100% the priority, which is why the vast majority of people in the first world have access to food primarily produced by a predominately private food industry.

The US is in the top 5 in the world for food security: https://www.worldatlas.com/articles/the-world-s-best-countri...

To the extent that there exists poor people unable to afford food, that’s a problem solved by welfare and subsidies, not by nationalizing food supply chains.

Apply that to healthcare.

> Feeding people is 100% the priority, which is why the vast majority of people in the first world have access to food primarily produced by a predominately private food industry.

This isn't true when feeding people is the _100% priority_, like in a big crisis, as a major war. The market flies out of the window and doesn't return before after the war is over since it just can't reliably produce goods in way that is appropriate for a crisis.

When the crisis is over however, the game of profit based production is restarted again.

No disagreements re: crisis / war. That's typically when states of emergency are conventionally recommended.

"Unable to afford food" = making money is a higher priority.

Unable to afford the doctor = making money is a higher priority.

Many food banks will give people who want food, food. Few questions, sometimes no questions asked.

It is rare to have similar access to health care.

Where did I mention nationalize?

I did not do that, and meant to not do that.

> Many food banks will give people who want food, food. Few questions, sometimes no questions asked.

> It is rare to have similar access to health care.

No it’s not, you just described Medicaid. The Venn diagram of people that rely on food banks for food and people on Medicaid is basically a circle.

Also, it’s debatable if food banks are a superior way of getting food to poor people, vs expanding food stamps and/or a UBI which can then be used at grocery stores.

> Where did I mention nationalize?

You seem to be attacking the profit motive as a mechanism by which to provision goods and services for which there is highly inelastic demand. My point is that if you think that the way we provision food is workable, then the concept of private healthcare with subsidies (basically the Swiss model and MAdv) should be as well. In both of those models, profit and “making money” is still key.

Medicaid asks a LOT of questions, the food bank does not.

You are saying a for profit system can work. You are about pricing in that scenario too. Fair enough. Given your perspective, your position on this is understandable. It is not agreeable, in my view.

Our way of provisioning food could be improved significantly. Food differs from health care significantly. Secondky, that we have food banks at all is deplorable and embarrassing.

That is all I care to entertain on that largely useless comparison.

I am attacking the profit motive in health care specifically because it carries an inherent conflict of interest and is poorly aligned with markets.

In any case, I am going to stop here for real, and just make it clear I do oppose health care as a market and do so because people do not have control over their need to participate and we all know what happens in a must buy, cannot walk away from the deal scenario: lowest value for the most possible dollars.

That comes up ALL the time and is why most of the world has removed that conflict of interest from the task of fixing sick or hurt people.

> Medicaid asks a LOT of questions, the food bank does not.

Not sure what you're talking about. Medicaid is extraordinarily generous. The only questions that are asked are in determining whether one qualifies to enroll in Medicaid, but once you're in, you pay almost nothing. Even prescription drugs are capped at $75, no matter how rare or fancy.

> Food differs from health care significantly

I disagree. Both are examples of critical goods/services without which humans die. They both factor into long-term health and quality of life.

> Secondky, that we have food banks at all is deplorable and embarrassing.

I completely agree. While I've been extolling the virtues of pricing systems to provision goods and services, I've also made it clear that welfare is extremely important. The US currently extends welfare to low income people, through SNAP/EBT food stamps, Section 8 Housing vouchers, and EITC. In my opinion, there's still room to further expand the generosity of these systems, and even consolidate them into a basic income.

It's worth disentangling welfare from public vs private, because it's easy to conflate the two. It's entirely possible to rely on private markets to bring down prices and increase availability, while using publicly funded welfare to enable access for those less fortunate.

I agree with you on decoupling the insurance from employment. Great move! This would immediately clarify what cost and risk exposure means to people too. Bonus!

I disagree on cost and risk exposure. Ask around both employer and employee about cost growth this last decade, for example... not getting lower, often are digit increases.

Regulation?

Well, the cap that limits margin dollars is easily dealt with by owning more of the chain of care. Opponents of this tepid method of cost control predicted it and it has happened. They can do billing with themselves and it works like tax shelters do and film studio accounting do to show compliant profit numbers.

From where I sit, being one of the really screwed set, I found some of what you put here clarifying, but did not find myself sold on the idea we are improving at all.

In fact, one way to differentiate the US profit motive from the rest of the developed world, is our continuing move toward market based care despite a lot of information

> did not find myself sold on the idea we are improving at all.

I don't think we're improving either; as long as we have an employer mandate and privileged tax treatment for group health insurance plans, I don't see this changing any time soon. All I'm saying is that the market/private nature of it has little to do with it. It's the "employer sponsored" nature of it that has everything to do with it.

> In fact, one way to differentiate the US profit motive from the rest of the developed world, is our continuing move toward market based care despite a lot of information

That the US is somehow undifferentiated in its pursuit of market based healthcare is not true at all; see Switzerland and the Netherlands. Both have purely private health insurance systems, and there's no sign of that changing any time soon, and both enjoy excellent health outcomes with broad approval of their respective healthcare systems. They're almost exactly as regulated as the US health insurance market, except with one glaring difference: the private health insurance is predominately purchased on the individual market. (https://www.forbes.com/sites/theapothecary/2011/04/29/why-sw...)

Not only that, Singapore has one of the most market-driven healthcare systems on the planet, and enjoys the status of being the most efficient healthcare system with some of the best outcomes:

https://www.bloomberg.com/graphics/infographics/most-efficie...

>I'm saying is that the market/private nature of it has little to do with it.

I appreciate that. And still do not agree.

Either the goal is fixing sick people, delivering care, or it is not.

If the US were to improve DRAMATICALLY on labor policy, among many areas needing improvement, perhaps we could show for profit being able to work.

Fair enough.

> Either the goal is fixing sick people, delivering care, or it is not.

That's an odd dichotomy. You could apply this to literally any good or service. With food, either your goal is nourishing hungry people, or it is not; and yet the private sector provides food just fine.

At the end of the day, price signals and market forces ensure that producers meet the needs of consumers. There are certainly instances of market failures, especially in the case of externalities. But with healthcare, there's really no evidence that markets and the private sector cannot deliver world class healthcare, and in fact we see evidence to the contrary, both domestically (in Medicare Advantage) as well as globally (in Switzerland and the Netherlands).

YES you can!

Not only is it interesting, it's reality.

You are making a market argument fundamentally.

With food, for example, people have lots of options, and while the need for food is absolute, wants for food can be ignored and or vary widely. Food wants is a great market. People can participate or not. They can prepare their own food or not.

Food needs are not as good of a market, though again, people have options, and are rarely in a must buy scenario.

That difference matters.

Notably, there is a cap on how big of a risk there is in the whole thing, and it's not all that big of a risk.

With food, one can end up in a weak scenario where one gets the least value for the most dollars. But, it's not typically life changing, and there are a lot of options for most people in most cases.

Contrast that with health care.

Let's talk about wants first, just like food. Cosmetics are a great example. People can choose not to do it. They may have options, depending on what the scenario is. This makes for a reasonable market. And, depending, people can make their own. I did that for a prosthetic a while back. Saved thousands of dollars. But, that is rare more than not. Still, we could empower people to some degree like we do with food.

Someone having a heart attack will need treatment, or let's say they are out of the market. It's not like they can shop around either. I could go through and compare / contrast with food, but here's the main point:

Unlike food, that doctor visit doesn't really have a cap on risk. 5 figure? 6 figure? 7 figure? All can and does happen.

And things people require? When people have to participate in the market, they pay the most and get the least value for the dollar. See insulin prices in the US?

Now, for a nation that has it's priorities in order and those priorities are not making money first and foremost, that price is a small fraction of what gets charged here in the US.

This is a shitty market. People are forced to buy, their choice is often limited, risks are crazy variant, costs not transparent, and on and on it goes.

At a minimum, most nations break these out making sure people who find themselves sick or hurt have baseline options that are not life changing, and market type options for those health care related things that make better sense.

Boil all that down, and what do we get?

Making money IS NOT THE TOP PRIORITY. Fixing sick people is.

When we examine all this in detail, we will find those shining examples of for profit health care actually working out are very well regulated, and that means they are forced to fix sick people first and foremost.

If they were not, then people would be tipping over for lack of ability to participate in the market, which isn't really even a market in a need scenario. It can be a market in the want scenario.

All of which is precisely why I frame it in those terms.

Which is it then?

Currently the US has chosen to make money first and foremost and look at the carnage!

> With food, for example, people have lots of options, and while the need for food is absolute, wants for food can be ignored and or vary widely.

Wants for food absolutely cannot be ignored. Without food, you starve and die. Along with healthcare, food is the quintessential example of a good/service with price inelastic demand.

Now, you’re absolutely correct that in most food markets, there is a variety of options; that’s exactly what’s needed for a market to function. Unfortunately you haven’t demonstrated that private health insurance markets are inherently devoid of such options by nature of their being private. Medicare Advantage is an extremely healthy market, as is the individual market in Switzerland.

> Unlike food, that doctor visit doesn't really have a cap on risk. 5 figure? 6 figure? 7 figure? All can and does happen.

You’re just talking about catastrophic risk here, and as I’ve already mentioned, there’s nothing inherent to the private insurance model that makes this unworkable. This isn’t based on guesses and conjecture, it’s based in empirical outcomes: see Medicare Advantage, Netherlands, and Switzerland. Also keep in mind that nobody here is arguing against subsidies for poor or unhealthy people; we’re just talking about whether that money is used to purchase plans are created by actuaries that work for the government, or for private sector organizations, and the merits of each.

> And things people require? When people have to participate in the market, they pay the most and get the least value for the dollar.

That’s true in the US. That’s not true in Switzerland. Both have private healthcare markets. Therefore, it’s impossible to conclude just based on US outcomes that private-ness is the root cause. It’s clearly something else.

> See insulin prices in the US?

The unfortunate reality here is that government-enforced patents allow insulin prices to remain bloated. Again the root cause isn’t the profit motive, that’s just a side effect.

> This is a shitty market.

Absolutely no disagreements there. The US healthcare market is indeed shitty (outside of Medicare Advantage at least), unlike Switzerland.

> People are forced to buy, their choice is often limited, risks are crazy variant, costs not transparent, and on and on it goes.

Agreed. Again, nothing to do with private-ness.

> When we examine all this in detail, we will find those shining examples of for profit health care actually working out are very well regulated, and that means they are forced to fix sick people first and foremost.

This is also true of the US. Health insurance is by far the most regulated industry in the country. Profit margins are capped by ACA, plans are regulated by ERISA, health insurance has minimum standards thanks to the ACA, insurers cannot deny access based on pre-existing conditions, and employers are mandated to provide health insurance — all thanks to the ACA. From a regulatory standpoint, the US is virtually identical to Switzerland, except for one notable difference: employer sponsored care.

> Again the root cause isn’t the profit motive, that’s just a side effect.

That's just utterly naive or politically biased. The profit motive is what causes those regulations in the first place! It has always been like that, especially in the states.

Don't try and create some sort of a fairy tale place where the profit motive won't try to rig regulations in their favor, they will always try to, this is what the incentive to make more and more profit creates.

> The profit motive is what causes those regulations in the first place! It has always been like that, especially in the states.

Actually, that's not true. The regulations were created as a result of a series of well-intentioned but catastrophic policy decisions, starting in World War 2. FDR instituted a wage cap to discourage private sector employment, which resulted in employers using non-wage benefits to participate in a competitive labor market. After a decade or so, it became an expected benefit (sort of like company cars, at the time). Eventually around the '70s, the Federal government decided it was time to incentivize remaining employers to provide health insurance by making premiums tax deductible; again a well-intentioned attempt to expand access. It finally all culminated in ACA which _mandated_ that employers provide health insurance. None of this can be attributed to lobbying, almost all of it attributed to well-intentioned regulations gone awry, not the profit motive.

In fact, the best shot we have right now of decoupling health insurance from employment is the ICHRA (https://ichra.com/), which allows employers to fulfill their healthcare obligations by providing tax-advantaged cash to employees that can be used to cover health insurance premiums on the individual market; and I imagine that came about due to lobbying.

Again, the easiest way to falsify a causal line between the profit motive and the current outcome is by finding instances of markets wherein there is a profit motive, but with differing outcomes. That's exactly what we see in Medicare Advantage, Switzerland, and the Netherlands.

> Don't try and create some sort of a fairy tale place where the profit motive won't try to rig regulations in their favor, they will always try to, this is what the incentive to make more and more profit creates.

No disagreements that industries will try to rig regulations in their favor. This is true everywhere in the world, and yet we see wildly differing outcomes. Notably, I'm yet to see an argument explaining away the Medicare Part A/B vs Part C outcomes in the US.

This is great info but I would add that when you talk about the unemployed left out in the cold, you also need to consider the self-employed. Having insurance tied to discrete employer risk pools makes insurance on the private market very expensive for all of those who cannot get subsidies (ACA/Medicaid). It really discourages people from taking the risks to start new ventures. When I started my business I went years without medical coverage.

Yes, agreed. A big part of why that's the case is that the individual market is one huge adverse sample, in its current form. Because most healthy people in the US are employed, they tend to receive their health insurance through employer sponsored group plans, which by design pools risk only within that group.

What ends up happening is that anyone left over has to participate in risky markets with higher premiums in general, resulting in the mostly broken state of the US individual market.

In contrast, you have countries like Switzerland where pretty much all health insurance is purchased on the individual market, and risk is pooled across larger and more diverse populations.

Do you have any insight into how a Medicare eligible person choosing Medicare advantage lose most of their funding for extended care facilities or SAR/AR days (in exchange for subpar vision/dental)? That 39% does not evaporate into savings.

Yes, Medicare traditionally never covered long term care. This was true for both Original Medicare, as well as for Medicare Advantage. Only Medicaid covered long term care, but as you may know that’s only for those with low income.

However, this is beginning to change. The Centers for Medicare and Medicaid Services has begun to allow private Medicare Part C insurers expand into long term care. Notably, to this day the public Medicare still doesn’t cover long term care, whereas M.Adv plans are beginning to.

If you’re talking about SNFs, Medicare Advantage is basically at parity with OM. I’ve seen Advantage plans by big payers like Humana with SNF benefit periods of 100 days. The 39% savings figure is on average, but if you require long term SNF stay, you’ll probably cost roughly the same to a private insurer as you do to Original Medicare. None of that stops private insurers from offering the benefit, since the actuarial math works out to the kind of savings that we’re mentioned above, across a whole benefit population.

> in exchange for subpar vision/dental

I’m not sure what data you’re looking at, but from where I sit, Medicare Advantage almost always includes vision/dental, whereas Original Medicare does not (which is why some seniors go for Medigap).

As a question, why haven't any of these techniques made waves outside the US? Other countries don't have the same monopoly/monopsony powers in the medical industries that are prevalent in the US.

US is exactly the place where those techniques would make waves because of what the US is paying for radiology; in countries where radiologists don't have the same monopoly/monopsony powers it's not nearly as lucrative to replace them.

For example, I'm distantly involved in a project with non-US-radiologists about ML support for automating radiology note dictation (which is a much simpler and much "politically cleaner" issue than actual radiology automation), and IMHO they and their organization would be happy to integrate some image analyis ML tools in their workflow to automate part of their work. However, the current methods really aren't ready, and the local market isn't sufficiently large to make the jump and make a startup to make them ready, that would have to wait until further improvements, most likely done by someone trying to get the US radiologists' money.

There's not really a way to disambiguate the two though - the fact that there are lots of medical technology startups and new drugs coming out of the US is because of the costs involved and how much can be harvested by being a little better. This creates new technologies that the US can't really protect against proliferation - so all of the money has to be harvested from the US market.

This isn't necessarily a bad thing - I for one happen to think it's great that our expensive medical system is financing all kinds of wonderful new technologies that benefit the world overall. However, the major problem here is that things that would be useful for other places simply don't have the market to support it, so most medical innovation exists in the context of the US medical system and it's problems - some of which are widespread, some of which are not. I do wish there were some other testbed healthcare systems out there for companies to try to disrupt, but I don't think it is (by itself) a call for medical reform.

My preferred medical reform is to "legalize insurance markets" (ie: repeal laws that state that insurance companies operating in state Y cannot sell insurance to people in state X because state Y policies are not legally compatible) and try to break the monopoly that doctors and nurses enjoy....somehow. Telehealth? Maybe?

But, is it? Almost half of the funding for Healthcare innovation is governmental even in the US, and a competent public health system already has a strong incentive to reduce costs. So if a technology has the potential to reduce costs, a more efficient healthcare system would also pay for it - and if one doesn't there are dozens that can too - and if there is no path to it providing value overall in such a system, then it's going to be on the balance less efficient anyways.

To note, a big issue in public innovation is that rich western countries, led by the US, HATE governments competing against the private sector. So if a government comes up with an innovative solution, they are generally disallowed from selling it, which hurts everyone except private companies.

This happened in my city a few years ago, which had a very early innovation in bike sharing, much before any VC funded bike sharing company, and other cities had expressed interest in paying my city for implementing this service locally.

But because of laws banning public endeavors from engaging in commercial activities, this was struck down, hurting all the taxpayers in my city, citizens of my city that would have benefited from better service from the experience, and millions of citizens from interested cities which would have received better service and who would have saved money.

Or another case in my province was the invention by the electricity utility of the electric hub motor - which is now over 40 years later in widespread use due to its efficiency and low cost - but instead of exploiting that patent and selling those things, it had to partner with a private company which got exclusivity and mostly squandered it. Again, hurting almost everyone which might have benefited from lower cost electric transportation as well as the taxpayer here.

This is actually part of why China is eating everyone's lunch, they used their size and never really agreed to these rules, leaving themselves the opportunity to profitably invent at the state level, leading into more efficient state owned enterprises that can often profitably outcompete the private sector.

> I for one happen to think it's great that our expensive medical system is financing all kinds of wonderful new technologies that benefit the world overall.

Does it factoring in situation of people unable to pay medical bills?

If the entire rest of the world isn’t a big enough market to be worth developing for then maybe we don’t need ml radiology we just need medical reform.

The entire rest of the world isn't a market, it's many separate markets that need to be entered separately by overcoming different moats. Market fragmentation matters, especially in regulated industries like medicine.

But yes, medical reform is definitely something that might be helpful - technological solutions almost always aren't the best way for solving social/political problems.

EU seems to have quite a lot of companies offering AI solutions in radiology:

https://grand-challenge.org/aiforradiology/companies/

Or the VA, which is a massive single-payer healthcare system that would love to cut costs.

> the largest source of bankruptcy in the US is medical costs

That's not what the article says.

"Two-thirds of people who file for bankruptcy cite medical issues as a key contributor to their financial downfall."

Those issues can absolutely include direct costs, but they also include things like not being able to work, needing a lot of day to day help, and other things that increase costs and reduce income even if the actual medical costs were largely covered.

I don't really know of one.. I don't think automation is ever shunned as long as it is useful and known to be useful. Everyone likes things that save time.

There is an essentially an unrestricted demand for healthcare across the world.. they will use the time to either talk to their patients more (or start to if they don't already).. or they will move into other medical fields.. or increase the volume of screening.. (may be harmful, but that's another matter). They probably don't want to do it as it won't really save them much time. OR it will save them time and they have been burnt before. For example, early voice recognition was very poor and over promised. Stopped me using it for ages after it became fairly good. It's still not actually better than typing, but it is closer now. Let's all focus on voice recognition that works before moving on to grander plans....

I wonder if you can replace a GP with a decision tree. You could update the tree as new research is done.

If you could collect reliable diagnostic data locally, you could serve this globally and for free.

It would also be a treasure trove of data about how we respond to various treatments.

> I wonder if you can replace a GP with a decision tree.

No, you can't.

> If you could collect reliable diagnostic data

And there's the reason. You can't do that either. There is a reason why GPs go through medical school.

> No, you can't.

Any sound reason, or are you either a) a defeatist, or b) a GP?

>There is a reason why GPs go through medical school

The input data would be basic things like:

- blood pressure

- weight

- images of the ear canals and throat

- blood, urine, saliva samples, perhaps analyzed in a regional centre

You don't need a ton of training to get the above from a patient and into a computer, and to ship the samples.

> Any sound reason

The job of a GP is actually probably one of the top hardest to automate, because the GP's main (and often only) job is to extract information. And that _does not_ consist in performing plenty of tests, but in speaking to and most importantly listening to the patient.

> You don't need a ton of training to get the above from a patient and into a computer, and to ship the samples.

Great! And you know what good that would do to improve diagnostic accuracy? Zilch. Zero. There's a saying that '90% of diagnoses are done on history'. Now tell me why that would be different for an algorithm given identical information? If there was a simple answer to that, we'd already be running statistical models over patient labs all day long, which we're not.

> are you either a) a defeatist, or b) a GP?

I'm an epidemiologist and also a practicing anesthesiologist, which is why the statistical theories of people who have never set foot in the clinics to see what's the job really about make me want to jump off a bridge.

When I go to the doctor, this happens:

- Doctor says "Say ahhh" and looks in my throat with the thingy

- Doctor looks in my ear canals with another thingy

- On other occasions, my other vitals are taken, maybe some vials of blood, etc. Again, a student can do this.

I'm asked a few general questions, with some follow-up questions based on my answers.

Then the doctor puts this information - along with my patient history - into the decision tree in their head and comes up with a result. If the doctor is stumped, I'm sent to a specialist.

The above can be automated, plain and simple. It would also be an improvement over my experience of the health system - in Canada. I have never seen my GP pull up a multi-year graph of my blood pressure, weight, or whatever. What I am describing is a system for creating regular data points of the kind currently used in diagnosis. What I fail to understand is how you cannot see that there must necessarily be predictive value in such a database.

Even if only 80% of the job can be automated, public health would improve immensely if the global population can do regular checkups like the above cheaply.

> What I fail to understand is how you cannot see that there must necessarily be predictive value in such a database.

I can see allright. But you cannot see that your hypothetical database is lacking most of the info because your doc actually mostly evaluates you by looking at your general composure and relying on X years of experience and a bit of knowledge shoves that into the really complex decision tree in his head: "Hmmmm... this guy looks mostly fine."

Now, you feed your database to the latest deep-learning shiny thingy that tells you: "this guy has X% chance of having a horrible cancer, but I can't explain why". So you enjoy many months of costly investigation because you don't want to miss something, right? And after the fact, it is discovered that the lack of standardization in measurements caused the algorithm to decide that the light hue in the office was a sign of cancer.

All that to say that yes, someday what you are imagining may well be possible, but we are really very far from having the technology to do that now.

I would have never thought of this, but I'm pretty sure gait, posture, and voice analyses can reliably be classified as "probably ill" or "probably well".

That's not really the hard or useful part part. According to a radiologist and machine learning researcher[1]:

"It turns out that deep learning is a very good match for some of the most time consuming (and therefore costly) parts of medicine: the perceptual tasks.

We also saw that many decisions simply fall out of the perceptual process; once you have identified what you are seeing or hearing, there is no more “thinking” work to do."

[1]: https://lukeoakdenrayner.wordpress.com/2017/05/03/the-end-of...

> examples

The taxi system, until Uber and Lyft kicked their ant hill.

The thing about Health Care is most efforts to automate it have failed. Arguably that's because no one "understands" the field, in the sense that no one can give, codified summary of the way they operate; each professional who's part of a health care pipeline takes into account twenty different common variabilities in human body/health/behavior/etc.

It's similar to the situation of self-driving cars, where the ability to do the ordinary task is overwhelmed by the existence of many, many corner cases that can't be easily trained-for. Except in health care, corner cases are much more common. Just seeking health care is an exceptional relative to something in ordinary life.

It's worse than even that.

The cartel arrangement is as described, but it's increasingly not even a great deal for the radiologists.

The business of radiology is increasingly centralized into teleradiography farms. That means that radiologists are working in shifts, and evaluated according to production metrics, like line workers in a factory.

The cartel arrangement will probably continue, as it is advantageous for people at the top of this food chain, but it's not an arrangement that's going to result in a lot of wealth and job security flowing to individual radiologists. Nor will it result in great outcomes for patients.

CTO of a CV+AI/ML startup developing a radiology solution eh? Let me ask you a couple of quick questions: What was your liability insurance like? How much coverage per diagnosis did you carry?

Let me make it simpler: How much blame was your company willing to absorb if your algorithm made a faulty diagnosis?

Great question! We did our trials at two overseas locations in parallel with doctors. All uses cases were diagnostic for immigration purposes (e.g., detecting Tuberculosis and other chest infections at border points of entry). Given the non-medical use -- no liability insurance. No coverage diagnosis. Also given everything was run in parallel, double-blind with doctors also doing reads, no blame had to be absorbed. Once we got out of parallel, still we wouldn't need liability.

The importance here was demonstrating efficacy, which we did fantastically well.

Once we prove efficacy for multiple use cases, we can at least remove the "oh you computer scientists dont get it" argument and can have adult conversations about how to progress state of the art rather than continue to bleed patients dry.

I'll admit there are definitely barriers like what you mention. But those barriers are not some impenetrable force once we break down real issues and deal with them separately and start treating the problem as one we can solve as a society.

I can't help but think some of the barriers here involved proving the software in a situation decidedly different than a clinical setting. I would not be surprised if an immigration medical officer developed different views about diseases than a GP or ER doctor. They're not treating the person, they're not in a doctor-patient relationship with the person, they're not really even "diagnosing" the person, they're just deciding whether they're "too sick" to come into the country. Maybe if the person looks messed up in some other way, their chest x-ray gets interpreted a little more strictly.

>> I can't help but think some of the barriers here involved proving the software in a situation decidedly different than a clinical setting.

Totally agree. But science moves in baby steps and progress builds on progress. We started ML by doing linear regression. Then we moved onto recognizing digits. Then we moved onto recognizing cats. Suddenly, Google Photos can find a friend of mine from 1994 in images it appears to have automatically sucked up. That is amazing progress.

Similarly, our viewpoint as co-founders in the space was to solve a single use-case amazingly well and prove AUC and cost/value metrics. The field wont be moved by me or you, it will be moved by dozens of teams building upon each other.

But AI theater being good enough to replace no-stakes (because no one is liable to anyone for any errors, in either direction) medical theater is a step, just not as big a step or relevant to any use case of any importance as being sold upthread

> Once we prove efficacy for multiple use cases, we can at least remove the "oh you computer scientists dont get it"

No, you can't. Stating this is a clear proof that you don't understand what you're dealing with. In medical ML/AI, efficacy is not the issue. What you are detecting is not relevant. That's the issue. But I know I won't convince you.

From where does the efficacy come if what you are detecting is irrelevant?

They are detecting what they are testing for. But that's in most cases irrelevant regarding what happens to the patient afterwards, because it's lacking major connexions to the clinical situation that will have to be filled up by a human expert.

So it does in fact work. Unfortunately, only in trivial cases.

Maybe, but then the problem isn't an issue with AI/ML, it's that humans just suck at math.

We're terrible at bayesian logic. Especially when it comes to medical tests, and doctors are very guilty of this also, we ignore priors and take what should just be a Bayes factor as the final truth.

We're terrible at bayesian logic all right, but still better than machines lacking most of the data picture. That's why the priority is not to push lab model efficiency but to push for policy changes that encourage sensible gathering of data. And that's _far_ more difficult than theorizing about model efficiency vs. humans.

So the answer is: zero. Not surprising.

Why does it surprise you then that doctors and patients don't take the solution seriously? You don't have any skin in the game! Whereas the doctors are on the hook for malpractice, and as for the patient, well it's life or death for them.

This is largely where the art of "labeling/claims" comes into play regarding how explicitly worded a "diagnosis" can be. There is a lot of room to play on the spectrum from truly diagnosing a patient with a disease (which requires the most evidence and carries the most liability) all the way down to gently prompting a healthcare provider to look at one record earlier than another one while reviewing their reading queue.

I'm pretty sure people said the same things (nothing will ever change, doctors will never advocate for or accept change) when radiology went from films to digital. I'm sure they said the same things when radiology went from having scribes to using voice recognition software (e.g. Nuance) for reports.

There seems to be a misconception that this is some kind of "all or nothing" thing, where AI will "automate away" radiologists. It's like a decade ago when everybody thought we were just about to "automate away" human drivers, except unlike driving, most radiology reads are by definition (i.e. a sick person) exceptional, out-of-baseline scenarios.

I think this is missing some things about radiology economics. There are indeed incentives to automate as much as possible, especially for outsourced radiology practices like Radiology Partners or people getting paid by pharma companies for detailed clinical trial reads. Organizations like these are getting paid a certain amount per read. If they can use software to speed up parts of their work while demonstrating effectiveness, they make more money. Eventually this drives down the price. There would still be a human in the loop to review or sign off on whatever the AI does, and to look for any anomalies that it misses. But there can be less time spent on rote work or routine segmentation, and more on the overall disease picture.

It's true the amount of imaging going on in the US has increased faster than both the population growth and the number of radiologists. At a certain point, the number of existing radiologists doesn't have time to read the images at any price. This gives the alleged cartel a few choices: graduate more radiologists, outsource reads, or use software to produce more output per radiologist. In the last case, which a self-interested group would obviously choose, they get paid the same but each individual patient pays less.

"...because seismic interpretation isnt effectively a cartel..."

I know some people who would disagree with you on that one!

Seriously, though, you're making an excellent point that I hadn't considered. Healthcare has a lot of "interesting" incentive structures and are baked-in constraints that would prevent even a perfect solution from being widely deployed.

It's not the same as geology, for sure, even though there are some parallels in terms in of image interpretation.

People have been trying to do this with expert systems, flow charts, and every other technology you can imagine, and have for decades. My wife is a pharmacist, and they have software that is supposed to help them out with the bewildering number of medicines that are out there now. This seems like a trivial case, compared to radiology: (here in the US) the FDA publishes guidelines, so just take those and turn them into code, but she finds it "not that much of a help" that mostly gets an override. "Every once in a while I'll get an alert that is helpful, but most of them are not helpful, even a little bit." "Mostly false positives."

And that's for a lot easier case than radiology.

Similarly, in infection control and antimicrobial stewardship, at this point pitching Yet Another Decision Support Tool will get you dirty looks.

> WHY ON EARTH WOULD THEY WANT THEIR WORK AUTOMATED AWAY?

Because any radiologist directly involved in the work of automating it away could capture multiple salaries.

> but mostly dragging their feet and hindering real progress, presumably because of the threat it posed.

It sounds more like they were not offered a stake, or were not sufficiently convinced it would work enough to accept a stake.

I agree with you and don’t know why people would think radiologists would be against automating their jobs away.

Most radiologists aren’t paid by the hour so it’s not like the longer it takes to review and diagnose the better. Having automation tools would allow a radiologist to do even more work and make even more money.

Unless someone literally thinks they won’t need an authoritative radiologist in the loop any longer. But that’s pretty silly since we can’t even automate a McDonald’s cook out of the picture.

>>> I agree with you and don’t know why people would think radiologists would be against automating their jobs away...Having automation tools would allow a radiologist to do even more work and make even more money.

I'd love to understand your viewpoint here. What you're describing would be awesome to a small segment of radiologists, but then what happens to the rest of them?

Further, why would the rest agree to it?!?! This isnt web ad sales or hosting where anyone can come in, do a better job, and win market share and get rich. Rather, here, the limited set of Radiologists would need to agree on standards of practice via the ABR -- why would they do that if it means most of them suffer as a result?

> awesome to a small segment of radiologists, but then what happens to the rest of them?

The same thing, they would do more radiology consults.

This is just like any other tech that’s improved radiology, it’s reduced the cost so there are more consults done.

Let’s say that a review takes 10 minutes without this new tech. With the new tech the review takes 30 seconds. So now a single radiologist can do 120 reviews an hour instead of 6. That’s great, as every radiologist will make way more money.

And also more radiology consults will be done because things that are too expensive and time consuming now become quick and cheaper.

Most of them don’t suffer, they do better.

Again, this isn’t a situation where any radiologists are getting fired and any solution that doesn’t involve radiologists is stupid since you still need humans to confirm and take responsibility for diagnosis or confirmation.

From what I've seen in clinical collaborators in Life Sciences, they're usually driven by pretty selfish incentives - publications, conferences, money, a way to push someone's pet theory if they happen to have one. They have major jobs, with patients and hierarchical games aside from R&D, and rarely care about research progress as such. (And research is mostly low quality, with obscure clinical perspectives). They don't need to actively obstruct, but they have enough reasons not to care.

For short term oriented VC funded startup any professional who likes to err on the side of caution is immediately looked upon as a corrupt actor hindering progress for personal gain.

You can just look at this from the outside w/o any startup's opinions: Why are some products and services' costs growthing faster and out of lockset with the rest of the economy. Some example niches come to mind: college textbooks, college tuition, specialty medical costs.

No one on the outside has to opine -- you can just look at the prices for some of these and know there are abnormal market forces at work.

That's some weird logic. Let's say the market is broken for textbooks. What does it have to do with what I wrote?

"Move fast and break things" is less compelling when the things in question is someone's grandmother.

I see a lot of parallels to this and airline control towers.

I think most can see that what control towers are doing is highly automatable (It's a queue) yet the industry has sabotaged it at every turn. Because the jobs it will eliminate are the ones that need to verify the system works correctly.

A similar thing happens with train operators. We are busy setting up self driving cars, yet a self driving train is almost a trivial implementation that we don't do because the train conductors would never get on board with such a system.

Self-driving subways do exist in a number of cities. But generally, train operator aren't a huge cost. While taxi driver serves 1-2 customers at a time, a conductor can easily move around a thousand people in a train. Or compared with trucks, freight trains can be ridiculously long and so paying one guy is really nothing in comparison.

>Because the jobs it will eliminate are the ones that need to verify the system works correctly.

I don't understand how you recognize that, but still blame them.

I don't ever want to trust pure algorithms or "machine learning" for my health. I believe there is just too much variability in humans, and the costs for outliers is going to be immense.

> because the train conductors would never get on board with such a system.

The train conductors wouldn't have any say in the operation.

Conductors union could clearly organize a strong opposition to this change. At least in France they proved they know how to do it.

“We will stop replacing leaving conductors” is nothing they can oppose to.

Well, I am guessing you are not an MD and as such you do not understand what radiology really is as a profession. You certainly have a very advanced technical knowledge about it, even much more than most radiologists. And that's precisely the catch: why are radiologists (mostly) non-technical people? The only possible answer is 'because what's asked of them as professionals is not technical'. As many (all) other specialties, radiology is more art than science. Its the science of interpreting images in context, and you can't separate the two.

So actually, radiology startups all fail on this crucial issue: to do a good job, you'll not only have to automate image interpretation, but really automate that of the whole EHR. And given the amount of poorly encoded information in there, machines fail now and will continue to do so in the foreseeable future.

No, Im not an MD, but my co-founder was.

Globally, hundreds of thousands of radiologists have been trained over the years have have collectively achieved generally consistent practices. Radiology is pattern matching and a set of very complex decision trees. They arent magic, because we consistently churn out more practitioners who achieve the same consistent outputs given inputs.

Anyone trying to improve things is like every other scientist, they aren't trying to figure out the entire decision tree or every single thing, they are trying to chip away on complex problems little by little.

I also strongly disagree with "radiology is more art than science" because if it was, radiologists wouldn't be able to agree on diagnoses.

Trivial radiology is pattern matching. Radiology for which you need a radiologist for is not. Yes, you could replace part of radiology and some radiologists with algorithms today. Unfortunately, those are also mostly the cases where we don't need radiologists.

I'm sorry, but in many cases and even many important diagnoses that impact the patient, 2 radiologists that aren't allowed to communicate will sometimes reach widely different conclusions. Additionally, if you've interacted with radiologists you were certainly able to see that their analytical and logical thinking capabilities are often far below that of a CS major.

A complex problem in radiology is not: "this patient has TB because [chest X-ray]". It's more like: "take this patient that had 30+ surgeries in the past 5y and tell me if his problem is more likely to be diagnosis 1 or diagnosis 2". And the problem for those cases is that you will never reach the critical mass of training data required (within the capabilities of a single startup) because those cases are too rare. You'll need the collaboration of the health system as a whole for that, _i.e._ state policies. So you can't really chip away at the complex problems on your own.

The reason a doctor is educated in general anatomy is so that when their specialty hits edge cases, they are able to reason about what could be the underlaying cause. This reasoning in a sparse information environment is exactly the scenario AI/ML is worst at.

If two radiologists can consistently reach wildly different conclusions then it really can only mean two things. Either or both radiologist are incompetent. There is not enough information available in the diagnoses tools we have to make a diagnoses.

In both cases I would say this counts against radiologist and for an AI solution. I would rather have a continuously improving AI then a coin tossing radiologist.

I don't see this as a response to the previous post at all. It was about the technical issues associated with a professional data interpreter, outside the simple image being interpreted. This is just cynicism about money and motivation.

Is Radiology affected by the same external factors as seismology? Does one image area depend deeply on surrounding features? Are there external rules that can override what the image seems to present?

> Further, Radiologists standards of practice are driven partly by their board (The American Board of Radiology) and the supply of labor is also controlled by them (The American Board of Radiology) by way of limited residency spots to train new radiologists.

Perhaps it is time to found the American Board of Computational Radiology (or Medicine)? There seems to be a chilling effect on tech innovation in the medical space in the US. On recent trips to the dentist, it seems like most of the cool new tech is coming out of Israel.

Interesting, how would the standards of the Board of Computational Radiology be set? Is the implication that the current standards are too high?

I’m not sure, but it removes the conflict of interest (and subsequent gatekeeping) created by radiologists who don’t want technology to automate away some of their work.

There's a lot to unpack here! In the USA, the ABR regulates human radiologists via board certification. Medical technology is traditionally regulated by the 510K, PMA, and de novo pathways at the FDA. Of course, these products still have to demonstrate value in order for major stakeholders (hospitals, radiology practices) to purchase them. And using these products does not absolve the ordering doctor, the radiologist, or the hospital of legal liability for misdiagnosis. In fact, IANAL and this is a somewhat novel area of the law, but any AI product that functions as a drop-in replacement for a radiologist might be held liable for misdiagnosis that leads to harm. These liabilities could become quite large for a product deployed at scale (a single misdiagnosis causing death can lead to a settlement in excess of 10 million dollars). In summary, there's quite a bit more to the issue than simple "gatekeeping." It might be appealing to blame radiologists for these issues, but there's a much larger system at work that's designed to ensure quality and safety for patients. This is not AdTech - lives are at stake and people can get hurt. Now, this definitely comes with a cost to innovation, but it's going to take more than just a few MD's to reinvent the economics and law of computers practicing medicine on people.

I have very little experience with the existing medical establishment, so forgive me if I've imagined a scenario that is not relevant. I would like to be able to go to have a scan done, receive the scan data, then have the choice of submitting that to a radiologist of my choice or to an AI service of my choice that can read and recommend next steps. It seems like such a scenario is stifled by the existing way of doing things.

My experience with getting a sonogram was that the sonogram wasn't that expensive, but getting it read was hugely expensive. I understand that there are issues of liability, but it's really frustrating that I'm saddled with a high deductible healthcare plan where access to useful medical stuff is stuck behind 3-4 digit costs. Want antibiotics, inhalers, ADHD meds - all of which are pretty cheap in generic form? Pay $100 to the doctor for the privilege. People have very little agency in this system.

I guess at the end of the day I'd like to see open data (I'm able to get the images/data from all kinds of scans & diagnostics), and some kind of transparent system for submitting my data for diagnosis or analysis. There may be caveats & waivers, but I'd be willing to pay $10 to an AI service to tell me "You definitely need to consult with a radiologist based on the data presented" before I pay a radiologist orders of magnitude more to tell me that everything looks OK.

You're making some very interesting and valid points. You've correctly identified that when receiving bundled services you lose the ability to negotiate or comparison shop on the basis of price. This is unfairly combined with a legal presumption that when the healthcare system generates a bill, the bill is valid until proven otherwise. Now, although we probably need more physicians, I don't think the limited supply of physicians is the primary reason for this situation - it's more due to increasing market concentration of health insurers on one side and hospital systems on the other, leading to regional monopolies that don't compete on price. In fact, with the decline of private practices and the rise of hospital systems, physicians receive less than 10% of all healthcare revenue. I can see that you'd like to unbundle your healthcare and regain control over prices, and I think that's a very reasonable thing to want.

The problem with your thesis is that many radiologists are paid per read. They're essentially contractors. These independent radiologists would gladly pay 100k/year for an AI that could do their jobs. Even if the tool didn't do diagnosis, just made them 50% faster, they would gladly pay a huge amount of money.

Contrast this with the field of Radiation Oncology. There are already AI auto-contouring solutions that you can buy today. It's a better fit given the limitations of AI:

1) Contouring can take hours per patient, so more value is gained by automating it

2) Normal structures are, well, normal; that is that they are not the diseased part of the patient. If the AI can't quite figure out where the kidney is and fills it out with an average looking kidney, hey that's probably an alright guess!

3) The AI doesn't have to be perfect; dosimetrists and docs can quickly check and update the result if the algorithm fails. Everyone is already familiar with this workflow due to the atlas segmentation algorithms that were state-of-the-art before.

4) Similarly, the output is easy to understand. If the AI did something crazy because of a failure to generalize, it will be obvious. Not so for a diagnostic AI.

5) Clinicians have the final say on which contours are used for treatment. The liability for contour assistance is lower for the software vendor.

The US healthcare system is slowly migrating from a fee-for-service model to a value-based model where at least some of the financial risk is shifted from employers and insurers to providers. The managers running those provider organizations thus have a direct incentive to adopt new technology if it actually works, even over radiologist objections. So far most radiology automation software hasn't generated a clear cost savings. That may change as technology improves.

Things are changing but as of 2019 by a whisker, most radiologists own their practices and accross all specialties 56.6 percent of physicians in the US are members of small practices. Physicians especially specialists tend to work for and with other physicians. In my area, at least in the recent past, all the cardiologists and all the urologists work for the same small practices organized around their specialties. I'd guess that tends to blunt pricing pressure on providers at least locally (see here for some stats https://www.mpo-mag.com/issues/2019-10-01/view_columns/a-loo...).

The general trend is that smaller practices are going away. More and more physicians are becoming employees of larger provider organizations. Small practices just aren't very viable any more because they lack the negotiating power to get high reimbursement rates from payers, and they don't have the economies of scale necessary to comply with interoperability mandates.

When new physicians complete their training fewer and fewer go on to start or join smaller practices.

Cost savings doesn't have to be near term. Imagine a doctor misses something on a reading and doesn't get the care they need... lawsuits are expensive. So you can have software which helps doctors do their job better which results in better patient outcomes. That is something hospitals are buying today for their radiology groups.

> where at least some of the financial risk is shifted from employers and insurers to providers

Do you have any evidence to back that up?

The ACA created a capitated payment system for medicare that providers can opt into. I'm not sure what evidence you're looking for other than the definition of capitation is "fee per patient" as opposed to "fee for service." Some states like California also have capitated plans on from private companies. https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-...

Here's one for you: https://innovation.cms.gov/innovation-models/bundled-payment...

Quality of care provisions in Medicare/Medicaid/ACA all help to shift shift costs to the practitioner if care is poor or has bad outcomes.

To balance that, do you have any comments on the arrogance and incentive problems in deep learning? :-P

Good points. But I genuinely wonder what role algorithm brittleness plays here.

“Fitting only to the test set” (see Andrew Ng quote in original article) is an acute concern in my circles: digital pathology in cancer research

See “Google’s medical AI was super accurate in a lab. Real life was a different story.”

https://www.technologyreview.com/2020/04/27/1000658/google-m...

>> In fact, most of our R&D and testing was done overseas in a more friendly single payer system.

So are CV/ML radiology systems deployed somewhere globally? Where, and how successful are they ?

And if not, why ?

> fee-for-service model for Radiology

It's not exactly a fee for service model that's the problem. It's the monopoly over the supply of labor.

Any business and union that manages to get its competition outlawed is guaranteed to abuse that position.

Family member worked on a board of a hospital, they raised charitable donations to buy more powerful software for the radiologist.

The radiologist can now work 10x faster and still bills the hospital the same amount.

The Doctor's Guild is exceedingly powerful.

I really wish Wallmart or Amazon would get into providing healthcare services on the long tail - a lot of common stuff.

I sounds odd but both those companies are built around ripping margins out of the value chain and not keeping much for themselves.

Ok, maybe not either of them ... but something like that: The 'Walmart of Healthcare' that revolutionizes cost.

Also - there are enough Medical Practitioners who would work there. Enough of them do care about patient outcomes, cost etc..

You would expect the radiologist to be paid less because he/she does more work now?

My salary did not went down when we migrated to more efficient tech.

You're misconstruing the parent's point. The radiologist can be paid the same, even more. Their point is that while the cost (at least in time) of the radiologist's work was cut to 1/10, individual patients' bills remained constant.

They get paid per review. They're now doing considerably more reviews thanks to the new software, paid for by donations.

Cost to patients is the same, but processed faster. Doctor making bank.

There is zero chance the software allowed the radiologists to work 10x faster... what?

Yeah that's my hyperbole. I should have said 'significantly faster'.

Yeah the article quoted gave very few details on the supposedly inconsistent performance of the model and lots of details on how few radiologists used it. Doctors (and other regulated professions) are a cabal that need to be broken up.

> Doctors (and other regulated professions) are a cabal that need to be broken up.

What do you see as the alternative to self-regulation? Some government office staffed with bureaucrats who have no idea about the realities of the actual work being done?

I got an engineering undergrad degree and had no interest in pursuing professional certification, but I certainly understand the importance of it for those practicing a in way that may harm the public's trust, and it made me appreciate the role that other professional bodies play in regulating who gets to represent themselves to the public as a lawyer, doctor, electrician, etc.

I am certainly not in favour of government offices staffed with bureaucrats. If anything I'd say that we should reduce the difficulty to be licensed as a doctor/lawyer/electrician. Let universities open more and larger med schools and have them be un-tethered from the self regulating bodies.

But surely regulating the profession is tightly tied to accrediting the education that is the gateway to it. How do you propose to "untether" these?

The universities handle the accreditation of all other faculties so why not have them also handle medicine law and engineering. As far as I'm aware theres no guild of philosophers who can control the number of students entering philosophy school. Universities would respond to supply and demand just like they do in any other faculty. Secondly make medicine an undergraduate programs , like it is in the most of the world which reduces the financial burden that doctors have to take on to complete the degree. This would also make the supply of doctors more elastic.

Would you say this is similar to challenges in other fields such as law?

Lawyers don't really control their own supply the way doctors do, which is why there is a great overabundance of people with law degrees in the country. AI has actually been used in a number of legal contexts, like building normalized contracts, or paralegal work. It's also because a lot of the highly paid legal work is pretty hard to automate in the same way, because it requires much more understanding of precedent or other nebulous ways of interpretation that AI isn't suited for.

> Lawyers don't really control their own supply the way doctors do

Bar associations do control standards for qualifications and acceptable on-ramp paths which directly governs supply (in fact, the oversupply differs in jurisdictions as a direct result of these decisions).

A key difference is that the legal pipeline isn’t sensitive to federal funding to govern supply of qualified new lawyers the way the medical pipeline is for doctors, though; there’s nothing analogous in law to the reliance on medicare funding of residency slots in medicine.

The medical pipeline doesn't have to be sensitive to federal funding either. There is nothing preventing residencies from being privately funded (besides the fact that most are currently publicly funded).

Medicare funds this out of a broad idea of it being a public good if there are more physicians. Note, there is no obligation that physicians work in public service after residency. This is in contrast to if you go to med school on a military scholarship (in which case, there is an obligation to serve).

In other words, if medicine weren't cartel, the government wouldn't need to pay doctors to train new doctors.

>> A key difference is that the legal pipeline isn’t sensitive to federal funding to govern supply of qualified new lawyers the way the medical pipeline is for doctors, though; there’s nothing analogous in law to the reliance on medicare funding of residency slots in medicine.

This a myth. Residency funding from medicare is an excuse because the funding is so limited and profits are so high. The real bottleneck here is the number of seats opened up by the specialty medical boards. Residents earn very little, under six figures, yet billings for residents are multiples of that. Even after resident stipends, benefits, tooling, and infra, i'm certain medical billings more than cover costs.

Partly -- law controls standards to some extent, but does not control supply necessarily.

Its probably a challenge for any profession where there is a legal monopoly where X service must be performed by Y individual, who also get to choose the quality of X and the number of Y in the market.

So, there are lots of countries with a shortage of cardiologists, some of whom could probably use any halfway effective AI solution if the alternative is no radiologist available at all. Perhaps this sort of thing should be started in a medium-income country rather than the wealthiest? Not the ones who cannot afford the equipment at all, but the ones whose trained radiologists keep leaving for wealthier countries.

So you're asserting that the reason your, and other, companies didn't do well is not that you couldn't live up to your promises but rather that there is a grand conspiracy to stop progress?

By the way, have you checked out https://timecube.2enp.com/https://timecube.2enp.com/?

>> grand conspiracy

Umm, I'm asserting like "Medicine is not perfect competition and thus prices are not competitive." If you want to think it is a "conspiracy" you can, but Economics offers great explanations for such setups. I think many of us in technology think all industries are driven by merit, cutting edge technology, margins, and competition.

In reality, not all industries are like this. This shouldnt be surprising. Computers go down in price. So do cloud service costs. So does RAM. But medicine stays expensive. "Conspiracy" is a shallow explanation -- it is just economics, it isnt perfectly competitive. And progress is hindered to maintain scarcity.

“Grand conspiracy” seems a bit uncharitable IMO. He’s just saying that the incentives aren’t aligned, which legitimately seems like an issue in this space.

It's sad you decided to give up because it wouldn't make money easily in the US. As you said, there are plenty of countries around the world that would have loved to lower medical costs without kowtowing to the doctors. Once it was a proven technology in places like Asia, I'm sure you would have been able to make it back into the US a lot easier.

Interesting, can you give an example of a radiologist hindering progress? You make an interesting point about radiologists setting practice standards - what alternative do you propose? You may also want to consider that radiologists don't determine practice standards in a vacuum - they have to serve the needs and expectations of their clinical colleagues.

It takes one group of great radiologists who have a bit of altruistic/capitalistic/venture side, doesn't it?

Yes. Or the right economic setup for societal gain where we can compete on value. Going from pay-for-service to value based care will be great. In the meantime, setups like https://www.nighthawkradiology.com/ are also great because they drive efficiency, I just wish they were more prevalent.

This is a really good point and example. I spent 10 years in mammography software, and I saw first hand how many outside factors can impact a physician's decision to biopsy or not a given artifact on an image.

Things like family history, patient's history, cycle timing, age, weight, other risk factors all play a role in a smart radiologist making the right decision for that patient. And the pattern recognition on top of that is really hard - it's not just about the pattern you see at a particular spot in an image, it's the whole image in the context of what that looks like. Could ML get better over time with this? Sure...but they've been using CAD in mammography for decades and it still hasn't replaced radiologists at all.

Could a model be made to include those over variables? Sure...but again the complexity of that kind of decision making is something that requires a lot more "intelligence" than any AI or ML system exhibits today and in my mind in the foreseeable future. Just collecting that data in a structured, consistent way is more challenging than people realize.

> I spent 10 years in mammography software, and I saw first hand how many outside factors can impact a physician's decision to biopsy or not a given artifact on an image.

This is slightly tangential, but I'm curious about your perspective on a classic example of medical statistical illiteracy. Whenever surveyed, the strong majority of doctors vastly overestimate the odds of a true positive mammogram (half of them by a factor of 10!!), due to flawed Bayesian thinking and the low base rate of breast cancer.[1]

Does your anecdotal experience contradict this data? If not, wouldn't two minutes of stats education (or a system that correctly accounted for base rate) utterly swamp intuition-driven analysis of tiny artifacts? Or is it simply that, through folk wisdom and experience, they implicitly adjust other terms in their mental calculus in order to account for this huge error in estimating one factor?

[1] https://blogs.cornell.edu/info2040/2014/11/12/doctors-dont-k...

The doctor diagnosing a patient isn't solving a puzzle of the kind posed here.

They are doing, as the previous comment said, interpretation. In practice, much of their thinking is profoundly rational and bayesian.

Human (and animal) thinking isn't primarily cognitive, ie., explicit reasoning. It is the application of learned concepts to sensory-motor systems in the right manner.

We don't look to dr's to formulate a crossword puzzle when a patient arrives; we look to them to be overly attentive to yellow skin when the patient's family has a history of alcoholism.

I'm not convinced this just couldn't be a large personal data set into an algorithm.

Doctors barely have any data as it is. I think personal bio testing and monitoring is going to be a huge market and medical paradigm shift.

would you rather have you heart rate and temp constantly monitored for months , or get it checked once a year by a GP to see if you have hypertension or any negative markers

With infinite amounts of perfect information and infinite computing time, all question-answer problems are trivially a hash-table lookup.

> In practice, much of their thinking is profoundly rational and bayesian.

Right, this was the third option I mentioned; I'm certainly not leaping all the way to the conclusion that one shouldn't listen to a doctor about the best course of action after mammogram results[1]. If their explicit understanding of mammography's false positive rate is so incredibly flawed, there is presumably an implicit counterbalance in the calculus that's built on experience (both their own and their mentors'/institution's), or an _order-of-magnitude_ error would show up in patient outcomes. I'd guess that this and the other instances of critical thinking failure that plague medical culture have their rough edges sanded over time, through decades of what is effectively guess-and-check iterative evolution, combined with institutional transmission of this folk wisdom.

Though I disagree that I would call this "profoundly rational", as IMO leaving explicit reasoning tools on the table instead of intentionally combining them with intuition/experience/epistemic humility is optimal. Iterative evolution is not an efficient process, and adding an attempt to explicitly model the functions you're learning can be a powerful tool. It's very difficult for me to imagine that a doctor explicitly internalized the basics of Bayesian reasoning wouldn't make them at least marginally more effective in terms of patient outcome, etc. Medical history is full of blind alleys built by medical hubris like your comment's "doctors know A Deeper Truth in their soul that defies explicit logical articulation". (Though I should note I don't claim to have a pat answer to this problem: one can theorize about improving a specific doctor's effectiveness, but scaling it to the whole culture is another, and can bump into everything from supply problems to downstream cultural impacts with negative consequences)

[1] Though with knowledge of flaws in such basic reasoning skills in one subpart of the total calculus, a patient can't rationally escape updating in the direction of checking their reasoning more thoroughly. Medicine is a collaborative endeavor between patient and doctor, both bring flaws in their reasoning to the table, and stronger evidence of a huge flaw in reasoning should lower confidence in the overall decision (though at a much lower magnitude, for the reasons we both describe here). This is the same logic that doctors use to rationally discount patient's opinions when they don't perceive them as coming from, eg, overly emotional reasoning.

Although we are tempted to think "automate away doctors jobs and take their salaries", it's more achievable and more valuable to provide existing doctors with better UI/UX which helps them make better decisions.

Bayesian probabilities could be displayed, other factors presented, and ML could be designed in an explainable way which helps the doctors. But that's too hard, requires too much talking to people, and too much respect for existing institutions, and not enough disruption! Sigh...

Yes, I agree. I was responding narrowly to the parent comment's premise, about full replacement (in theory). It's still useful to think about as a theoretical threshold, even if the path to commercialization is almost definitely through assistance.

This isn't really surprising to me considering that it's not usually a doctors job to estimate the odds of a true postive - false positives are far less of a problem than false negatives when doing an initial scan for cancer.

Biopsies aren't cost- and risk-free, including from a patient health and mental health perspective. On top of that, the decision space isn't simply binary: there are multiple types of biopsy procedure, and understanding (even implicitly) the probabilities in play can guide those decisions.

The Cult of the Doctor never ceases to amaze (and frankly disgust) me. I think it's absolutely a noble profession, and that doctors are generally smart and conscientious. Even some of the classic complaints are understandable. Eg, my incredibly kind, humble, and open-minded brother-in-law occasionally betrays some extreme disdain for his patients, a common complaint about medical culture. I continue to be a little put off by it, but I understand that he has to deal with some incredibly stupid things from most patients, and it's easy to see how taxing maintaining the welfare/autonomy balance can be. I try to keep this in mind whenever a doctor has an initial defensive reaction to my attempts to be an engaged and informed patient.

But, the lengths to which people bend over backwards to make flimsy excuses (like your comment) for massive errors in reasoning is just astonishing. I can't think of a single other context in which people so desperately clutch their pearls at any suggestion that a professional culture or institution could be improved.

Doctors are people, and people have flaws. They go through a training and acculturation process, which may also have flaws. This shouldn't be controversial. The only reason I can think of is that patients are no better at dealing with uncertainty than doctors are; the idea that you're being treated by a deity levitating in absolute epistemic certainty is more comforting than the idea that you're dealing with a smart, conscientious, trained human that nonetheless has human limitations (and that the field of medicine itself has plenty of uncertainty).

In my field (space) we have an unspoken mantra that autonomous systems should aid human decision making.

It's just so much easier to build a system that allows a human to focus on the tough calls, than it is to build an end-to-end system that makes all the decisions. Only in the most extreme examples does full autonomy make sense.

If there were one doctor in the world, I'd build an autonomous mammogram machine and have him periodically audit the diagnoses. Otherwise, better tools is the way to go.

I noticed this when visiting the OBGYN for sonograms to check the development of our children. The tools are really good. You can easily measure distances and thicknesses, visualize doppler flow of blood, everything is projected from weird polar space (what the instrument measures) to a Cartesian space (what we expect to see), and you can capture images or video in real time.

Sure, the cabal factor is real, as is the curmudgeon doctor, but I think we should be building tools, not doctors. We know how to build doctors.

Building tools to aid humans seems like the best of both worlds. This is already happening in some radiology subspecialties: The autonomous systems can highlight potential areas of interest but it’s up to humans to make the final call. For some cases it’s a quick and easy call, but for tougher calls the radiologist can bring other factors into consideration.

A counter example from your field: Would a human add value adjusting the boosters to vertically land starship?

Not a counter example, but a legit question in spirit since autonomy vs human will always be a reasonable choice for some parts of a system. I just don't think real-time control of a system that's never been flown by humans is a good example of where that choice is hard. You can describe it with equations, solutions are well understood, there's no available humans that can do it, there's a well-understood need because fly by wire was necessitated to control some aircraft, and will be necessary to control more air/spacecraft in the future. Classic computer problem.

(But I'm guessing Starship will have human controllers in/on the loop to do interventions before or during landing if required.)

Doctor diagnosis is a good example of when that choice is hard to make. Hard to describe with equations, hard to model, not a well-understood need since professional doctors exist, decades of experience has been accumulated, huge swaths of industry are dedicated to helping doctors, etc etc.

What I meant by "Allow the human to focus" definitely should be interpreted to mean removing small, multi-dimensional, fast, annoying things like real-time stabilization during landing.

I see the same thing in natural language processing. A lot of important details come from outside the four corners of the document.

Ironically, I often find myself in the unenviable position of being the machine learning person who's trying to convince people that machine learning is probably not a good fit for their problem. Or, worse, taking some more fuzzy-seeming position like, "Yes, we could solve this problem with machine learning, but it would actually cost more money than paying humans to do it by hand."

Part of why I hate hate hate the term "AI" is because you simply can't call something artificial intelligence and then expect them to understand that it's not actually useful for doing anything that requires any kind of intelligence.

You are absolutely correct. In fact, most NLP software ignores the formatting of documents which conveys a lot of information as well. For example, section headings must be treated differently from the text that makes up the body of a section. Its very hard to even determine section headings and then its hard to take advantage of them since the big transformer models simply accept a stream of unspecialized tokens.

There's an old AI joke that all actual problems reduce to the artificial general intelligence problem---everything else, by definition, doesn't require intelligence.

There's some truth to that. But I'd also argue that there's a tendency to try to bill every single kind of spinoff technology that the artificial intelligence community has produced as artificial intelligence.

Which a bit like characterizing mixing up a glass of Tang as a kind of space exploration.

But isn't this just what ML should be good at - taking a huge number of data points and finding the patterns? Or are you saying it's not an issue of ML working poorly but rather one of there not being a good enough data set to train it on properly?

One of the main tasks that doctors do is take patient's vague and non-specific problems, build up a rapport with them, understand what is normal and what is not, dealing with the "irrelevant" information present at the time, and focus the results into a finite tree of possibilities.

In principle, this would be a great task for an ML algorithm. It's all conditional probability. But every such system has failed to do that well -- because the "gut feeling" the doctor develops is funded by a whole host of prior information that an ML algorithm won't be trained on: what is "normal" for an IV-drug addict, or a patient with psychosis; how significant is the "I'm a bit confused" in the middle-aged man who was cycling and came off his bike and hit his head? Do the burns on the soles of this child's feet sit consistently with the story of someone who ran over a fire-pit barbecue "for fun", or is it a non-accidental injury? It's a world of shades of grey where, if you call it the wrong way, ultimately someone could die. Doctors do Bayesian maths, and they do it with priors coming from both their own personal experience as a member of society, and professional training. That is, in my ignorant opinion, the main distinction between what I do -- oft-called "academic medicine" or "academic radiology" -- and clinical medicine. The former looks at populations. The latter looks at individuals.

In other words, I don't think it's even possible to codify what the data the ML algorithms should be trained on -- they're culturally specific down to the level of an individual town in some sense; and require looking at huge populations at others.

> In other words, I don't think it's even possible to codify what the data the ML algorithms should be trained on -- they're culturally specific down to the level of an individual town in some sense; and require looking at huge populations at others.

This is inciteful. ML is by definition generalizing, and should only be used where it's ok to generalize. There is an implicit assumption in use cases like medical diagnosis that there is a latent representation of the condition that had a much lower dimensionality than the data and that the model is trained in such a way that there are no shortcuts to generalization that miss out on any information that may be important. The second condition is the hardest to meet I believe, because even if a model could take in lots of outside factors, it probably doesn't need to to do really well in training and validation, so it doesn't. The result is models that generalize, as you say, to the population instead of the individual, and end up throwing away vital context to the personal case.

I also believe this is an important consideration for many other ML applications. For example those models that predict recidivism rates. I'm sure its possible to build an accurate one, but almost certainly these models stereotypes in the way I mention above, and do not actually take the individual case into account, making them unfair to use on actual people.

On codification:

I actually disagree with this, but only slightly.

Imagine if instead of face to face, doctor transactions were via text. The questioning of the doctor can be monitored and patterns in decision trees observed could be codified, and weighed against the healthcare outcome (however defined).

What is missing, however, is the Counterfactual Reasoning. The "why" matters. The machine cannot reduce the doctor's choice of decision trees from all possible combinations, only that which it observes the doctor perform.

Tail-cases like rare genetic disorders would often be missed.

Tail-case like rare genetic disorders are often missed by doctors too. I have several friends who had Lyme disease with fairly serious complications (in the Northeast) (Not that Lyme disease is that rare - it's actually much more common than is expected). Each of them got misdiagnosed for multiple years by multiple different doctors until finally getting the correct diagnosis/treatment. So every system is fallible.

Personally, my exlerience with doctors is neither rapport nor nuanced understanding of my specific situation.

That is really not what they do, are trained to do or have time to do.

"There's not enough good data to train it on properly"

Bingo: You're in _very_ data poor environment, compared to something like predicting a consumer's preferences in videos or identifying and segmenting a bicycle. The external data is also very qualitative and hard to encode into meaningful features.

Right, but "the training data is bad" is a very ML centric way of looking at the issue. It pushes all the difficult parts of the problem into the "data prep" sphere of responsibility.

Note that there are different ways in which data can be bad (i) image resolution not good enough, too many artifacts and noise (ii) its woefully incomplete, doctors collect and use information from other channels that aren't even in the image, regular conversations, sizing up the patient, if the doctor knows the patient for a long time then a sense of what is not normal for the patient given his/her history etc., etc.

Some of the issues that have been discussed in the thread can be incorporated in to a Bayesian prior for the patient, but there is still this incompleteness issue to deal with.

The first step would be to build an information collection pipeline that is in the same league as the doctors. That alone will be a monumental effort because doctors have shared human experiences to draw from and they are allowed to iteratively collect information.

I'm just complaining that it seems fantastically reductive to call the absence of such a pipeline "bad data" because developing such a pipeline would be a thousand times the effort of implementing an image detection model. Maybe a million times. It will require either NLP like none we have seen before or an expert system with so much buy-in from the experts and investors that it survives the thousand rounds of iterative improvement it needs to address 99% of the requirements.

Comparing issues like low resolution and noise to such a development effort seems like comparing apples to... jet fighters.

How else would you describe the issue?

Structural. The problem hasn't even been correctly formulated yet -- and it will take an enormous amount of work to do so.

The gap is interpretation and application of those patterns. Building expert systems is expensive, but ML hits the low hanging fruit of showing patterns to experts out of the park.

ML should be good at drawing basic conclusions. End users are misunderstanding the boundary between basic and advanced.

Or, to put it another way, everyone agrees there's a difference in value and quality of output between an analyst with 1 year & 10 years of experience, right? So why are we treating ML like it should be able to solve both sorts of problems equally easily?

I have faith it will get there. But it's not there yet, in a general purpose way.

Because people like Hinton are outright saying that it already is there.

Incorporating more variables into the model wouldn't be sufficient. You would also need to get the input data for those variables into a form that algorithm could consume. Often the raw data simply isn't recorded in the patient's chart, or if it is recorded it's in unstructured text where even sophisticated NLP struggles to extract accurate discrete clinical concept codes.

Mammography is one of the most difficult to interpret. You need more data like the age and family history to decide on the next step.

Radiology is huge. I am sure ML can help in some of the specialties (it does not need to be all or none). The reason it is not is because of the medical system refusing to give in.

Surely you've seen the improvement over the last 5-6 years from machine learning in all the interpretation toolsets. The last place I worked we internally had a seismic inversion tool that blew all the commercial suites out the water. I'm currently contracting for an AI/ML service company currently that has a synthetic welllog tool that is can apparently beat the pants off actual well logging tools for a fraction of the cost (though I'm not a geologist or petrophysicist so I can't personally verify this.)

I think the problem is more the media and advertisers likes to paint the picture of a magical AI tool which will instantly solve all your problems and do all the work instead of a fulcrum to make doing the actual work significantly easier.

Bluntly, no. There hasn't been an improvement. At all.

We've been using machine learning in geology for far longer than it's been called that. Hell, we invented half the damn methods (seriously). Inverse theory is nothing new. Gaussian processes have been standard for 60 years. Markov models for stratigraphic sequences are commonly applied but again, have been for decades.

What hasn't changed at all is interpretation. Seimsic inversion is _very_ different from interpretation. Sure, we can run larger inverse problems, so seismic inversion has definitely improved, but that has no relationship at all to interpretation.

Put another way, to do seismic inversion you have to already have both the interpretation _and_ ground truth (i.e. the well and a model of the subsurface). At that point, you're in a data rich environment. It's a very different ball game than trying to actually develop the initial model of the subsurface with limited seismic data (usually 2d) and lots of "messier" regional datasets (e.g. gravity and magnetics).

I am wondering (knowing nothing about this) if there is an issue with the approach to acquire data that it putting AI in a difficult position. This is akin to trying to train and AI to walk in the footsteps of a geophysicist, rather than making new footsteps for the AI. I guess I would extend this to radiology too since it seems to be the same issue.

Let me give an example:

People often mention that truck drivers are safe from automation because lots of last mile work is awkward and non-standard, requiring humans to navigate the bizarre atypical situations the truck encounter. Training an AI to handle all this is far harder than getting it to drive on a highway.

What is often left out though is the idea that the infrastructure can/will change to accommodate the short comings of AI. This could look like warehouses having a "conductor" on staff who commandeers trucks for the tricky last bit of getting on the dock. Or perhaps preset radar and laser path guidance for the tight spots. I'd imagine most large volume shippers would build entire new warehouses just to accommodate automated trucks.

A long time ago people noted that horses offered much more versatility than cars since roads were rocky and muddy. How do you make a car than can traverse the terrain a horse does? You don't, you pave all the roads.

Automatic interpretation has been a thing for decades and the promise of replacing a geoscientist completely is always just over the horizon. Even with DL. The new tools are better yes, but honestly I wouldn’t invest in this space. Conventional interpretation is dead in the US. All the geos got laid off.

I’m going to call bullshit. No artificially generated well log is going to ever be better than a physically measured log.

I agreed with you up until the last paragraph. Generating data that cannot be told apart from any real data, cave ins and all, is probably one area where this can succeed.

Your other comment about picking the horizon of interest is really on point, that's where automated interpretation as it's been buzzworded to hell to date as had had no chance and has never lived up to how it was pitched. Many tools just made the problem worse.

That fact that this might change in some distant future, well it may be solved in some capacity but is it really worth the effort? Given that exploration has a limited future.

I have but one upvote to give, but as someone who worked as an interpreter and then moved onto the software side this is the problem that 99% of people don’t get.

You can train a DL model to pick every horizon, but you can’t train to pick the horizon of interest. Same with faults. Let’s not even get started with poorly imaged areas.

IMO a part of the problem here is that you have a misunderstanding on the part of deep learning people. They look at radiology, and they say "these people are just interpreting these pictures, we can train a deep learning model to do that better".

Maybe there's a bit of arrogance too, this idea that deep learning can surpass human performance in every field with enough data. That may be the case, but not if you fundamentally misunderstood the problem that needs to be solved, and the data you need to solve radiology, for instance, isn't all in the image.

Somewhat related: another area where DL seems to fail is anything that requires causal reasoning. The progress in robotics, for instance, hasn't been all that great. People will use DL for perception, but so far, using deep reinforcement learning for control only makes sense for really simple problems such as balancing your robot. When it comes to actually controlling what the robot is going to do next at a high level, people still write rules as programming code.

In terms of radiology and causal reasoning, you could imagine that if you added extra information that allows the model to deduce "this can't be a cancerous tumor because we've performed this other test", you would want your software to make that diagnosis reliably. You can't have it misdiagnose when the tumor is on the right side of the ribcage 30% of the time because there wasn't enough training data where that other test was performed. Strange failure modes like that are unacceptable.

Expanding on this, particularly regarding causal reasoning and rules, what I find especially puzzling is the desire to apply deep learning even in cases where the rules are explicitly known already, and the actual challenge would have been to reliably automate the application of the known, explicitly available rules.

Such cases include for example the application of tax law: Yes, it is complex and maybe cannot be automated entirely. However, even today, computer programs handle a large percentage of the arising cases automatically in many governments, and these programs often already have automated mechanisms to delegate a certain percentage of (randomly chosen, maybe weighted according to certain criteria) cases to humans for manual assessment and quality checks, also a case of rule-based reasoning. Even fraud detection can likely be better automated by encoding and applying the rules that auditors already use to detect suspicious cases.

The issue today is that all these rules are hard-coded, and the programs need to be rewritten and redeployed every time the laws change.

There's a perception in the DL field that encoding things into rules is bad, and that symbolic AI as a whole is bad. Probably because of backlash following the failure of symbolic AI. IMO the ideal is somewhere in the middle. There are things you want neural networks for, and there are also things you probably want rules for. The big advantage of a rule-based system is that it's much more predictable and easier to make sense of.

It's going to be very hard to engineer robust automated systems if we have no way to introspect what's going on inside and everything comes down to the neural networks opinion and behavior on a large suite of individual tests.

> The issue today is that all these rules are hard-coded, and the programs need to be rewritten and redeployed every time the laws change.

The programs are probably not being rewritten from scratch. I would argue that: the laws are, or basically should be, unambiguous code, as much as possible. If they can't be effectively translated into code, that signals ambiguity, a potential bug.

I wasn't alive in the 70s, but it feels like there's a counter-bias against expert systems borne out of those failures.

"If you're putting in rules, you're don't know how to build models."

But that's probably the difference between people having success with "AI" and banging their heads against the wall: do what works for your use case!

I have once seen an AI tool to determine what needed to be reported.

I found this remarkable, as there were clear (yet complex) rules on what needed to be reported, otherwise even the regulator wouldn't know what it was supposed to check.

I recall stories of IBM Watson's failures were focused around how they sold it as just dumping data into the machine and wonders coming out.

Meanwhile actual implementation customers weren't ready / were frustrated with how much prepping the data was required, how time consuming it was, and in a lot of ways how each situation was a sort of research project of its own.

It seems like any successful AI system will require the team working with the data to be experts in the actual data, or in this case experts in radiology ... and take a long time to really find out good outcomes / processes, if there are any to be found.

Add the fact that the medical industry is super iterative / science takes a long time to really figure out ... that's a big job.

There's no free ride, ML is data centric, you got to get close and personal with the data and its quality. That means 90% of our time is spent on data prep and evaluations.

Getting to know the weak points of your dataset takes a lot of effort and custom tool building. Speaking from experience.

And maybe look for things that are not expected...

My dad went to take a shoulder x-ray in preparation for a small bit of surgery. In the corner of the image the radiologist noticed something that didn't look right. He took more pictures, this time of the lungs, and quickly escalated the case.

My dad had fought cancer, and it turned out the cancer had spread to his lungs. He had gone to regular checks every six months for several years at that point, but the original cancer was in a different part of his body.

For a year prior he'd been short of breath, and they'd given him asthma medication... until he went to get that shoulder x-ray.

As a cancer patient that feels like negligance.

I agree. Essentially the same scenario has happened twice in my close circle since my dad.

Sadly it seems treatment here is very much focused on the organ, not the patient.

Hence why I tell people I come across who's diagnosed for the first time: learn where your cancer might spread to, and be very vigilant of changes/pain in those areas.

I guess some animals are also good at seismic interpretation. For radiology we first need to beat pigeons: https://www.mentalfloss.com/article/71455/pigeons-good-radio... (there was a HN post I think on this)

Actually mammography screening is done to my knowledge with out any background which could bias the decision. But here humans are fast anyways and even pidgins don't promise a relevant price cut. When complicated decisions need to be made . E.g on treatment we will have other problems with ai...

We used AI to analyze seismic data in the DARPA nuclear test monitoring system in the 1980s. I don’t think that it was considered to have anything but a fully automated system. That said, we had a large budget, and great teams of geophysicists and computer scientists, and 38 data collection stations around the world. In my experience, throwing money and resources at difficult problems usually gets those problems solved.

Very different sort of seismic data, FWIW.

You're referring to seismology and deciding whether something is a blast or a standard double-couple earthquake. That's fairly straightfoward, as it's mostly a matter of getting enough data from different angles. Lots of data processing and ambiguity, but in the end, you're inverting for a relatively simple mathematical model (the focal mechanism): https://en.wikipedia.org/wiki/Focal_mechanism

I'm referring to reflection seismic, where you're fundamentally interpreting an image after all of the processing to make the image (i.e. basically making a mathematic lens) has already been done.

I don't know anything about seismology, and I am going to put aside the money and focus on the math.

> The features you're interested in are almost never what's clearly imaged -- instead, you're predicting what's in that unimaged, "mushy" area over there through fundamental laws of physics like conservation of mass and understanding of the larger regional context. Those are deeply difficult to incorporate in machine learning in practice.

I was part of a university research lab over 15 years ago that was doing exactly this [1], with just regular old statistics (no AI/ML required). By modeling the variability of the stuff that you could see easily, you could produce priors strong enough to eek out the little bit of signal from the mush (which is basically what the actual radiologists do, which we know because they told us). It isn't a turn-key, black box solution like deep learning pretends to be. It takes a long time, it is highly dependent on getting good data sets, and years of labor goes into a basically bespoke solution for a single radiology problem, but the results agree with a human as closely as humans agree with each other. You also get the added bonus of understanding the relationships you are modeling when you are done.

From university lab to clinically proven diagnosis tool is of course a longer road, and I have not been involved in these projects for a long time, but my point is that the math problem on its own is tractable.

[1] http://midag.cs.unc.edu/

Funny, when I was young my father who was a geophyisicst (now retired) took me to seismological surveys, where we hit with a hammer on a plate to generate shock waves and measure velocity (mostly for building tunnels through montains). After a hard day of physical work, my father was drawing these lines at specific points of the recorded dataset (sorry I lack the proper terms and vocabulary because it was like 20 years ago) and there are some patterns you can use to identifiy those spots but most of it was based on his intuition and years of experience. Sometimes he let me do the interpretation and all I had was the those patterns, but many times he had to correct it because of what you describe (experience, terrain, environment, etc)

I wonder how often these projects truly need someone with on the ground experience guiding it, as the textbook tasks as you say are easy for even the humans

Interesting perspective. What's your take on tools that use AI/ML to accelerate applying an interpretation over a full volume? For example: https://youtu.be/mLgKtmLY3cs

Bluntly, they're useless except for a few niche cases.

Anything they're capable of picking up _isn't_ what you're actually concerned about as an interpeter. Sure they're good at picking reflectors in the shallow portion of the volume. No one cares about picking reflectors. That's not what you're doing as interpreter.

A good example is the faults in that video. Sure, it did a great job at picking the tiny-but-well-imaged and mostly irrelevant faults. Those are the sort of things you'd almost always ignore because they don't matter it detail for most applications.

The faults you care about are the ones that those methods more-or-less always fail to recognize. The significant faults are almost never imaged directly. Instead, they're inferred from deformed stratigraphy. It's definitely possible to automatically predict them using basic principles of structural geology, but it's exactly the type of thing that these sort of image-focused "automated interpretation" methods completely miss.

Simply put: These methods miss the point. They produce something that looks good, but isn't relevant to the problems that you're trying to solve. No one cares about the well-imaged portion that these methods do a good job with. They automate the part that took 0 time to begin with.

You seem extremely biased against AI in general, to the point where I very much doubt anybody would benefit from hearing your opinions on it.

I work in machine learning these days. I'm not biased against it -- it's literally my profession.

I'm biased against a specific category of applications that are being heavily pushed by people who don't actually understand the problem they're purporting to solve.

Put another way, the automated tools produce verifiably non-physical results nearly 100% of the time. The video there is a great example -- none of those faults could actually exist. They're close, but are all require violations of conservation of mass when compared to the horizons also picked by the model. Until "automated interpretation" tools start incorporating basic validation and physical constraints, they're just drawing lines. An interpretation is a _4D_ model. You _have_ to show how it developed through time -- it's part of the definition of "interpretation" and what distinguishes it from picking reflectors.

I have strong opinions because I've spent decades working in this field on both sides. I've been an exploration geologist _and_ I've developed automated interpretation tools. I've also worked outside of the oil industry in the broader tech industry.

I happen to think that structural geology is rather relevant to this problem. The law of conservation of mass still applies. You don't get to ignore it. All of these tools completely ignore it and product results that are physically impossible.

Incidentally, I don't even mean to pick on that video specifically. I actually quite deeply respect the folks at Enthought. It's just that the equivalent functionality has been around and been being pushed for about 15 years now (albeit it enabled via different algorithms over time). The deeper problem is that it usually solves the wrong problem.

I'm confused. You're saying they haven't incorporated basic physics into their model? How come? They don't have any geophysicists on staff?

>Many companies/etc keep promising to "revolutionize"

It is all about the money baby

If u fall and go to the ER, u get an xray to rule out a fracture. Many times the radiologist will read it after you leave the ER, yet he gets paid.

If u think ML cant read a trauma xray, and offer a faster service, you are wrong!! The problem is who gets paid, and who is paying the malpractice insurance

Check out in China, they have MRI machines with ML built in. U get the results before u get dressed!!

Who do you think I’d rather go after for malpractice? Someone that went to school for many years dedicated to medicine or the idiot stiffs behind a machine that can’t even spell the word “you”. That is in large part also what it is really about.

Having said that I do ML research on cross-sectional neuroimaging, and basically everything you said is nonsense.

So it seems that instead of an image recognition algo we need to feed years of univ education into the AI.