You can hardly conclude that broadly screening populations are ineffective from this study. You have to consider, among other things, the treatments available for the given disease being screened and the cost of that screening program. If treatments for the disease already have a low success rate (what is low?), the timing of detection doesn't really help. Additionally, if the cost of the screening program is negligible (what is negligible?), then even successfully treating a few patients may be worth it.

The current consensus about over-diagnosis (as I understand it) is that when there is a significant false positive rate and the cost of proving the positive false is high (in money, time, effort, worry), the screening program is not helpful. Some go further to say that low cost screening drives some of the high cost to outcome ratio in the US. I'll try to find a cite in my textbooks if you are interested.

I think the issues are deeper than that of false positives. Its possible that transient diseases get detected that would have fixed themselves without any treatment. Insead of a non-treatment one now has to deal with the side-effects of the interventions applied.

This is exacerbated by the fact that if the AI told the doctor that there is a doubt, no doctor will take the risk of not doing a biopsy / scanner / MRI / surgery (depending on the case). Because, how would you defend yourself in front of the judge ? This is something we always have in mind.

This is how you end with false positives and over-diagnosis.

This is a false blanket statement. Also one that could change as we start to see human+ai performance be better than just human performance.

For lung cancer screening, NLST showed a 20% reduction in mortality and now NELSON has shown even stronger results in Europe.

This “all screening is bad” is FUD in the medical field, frankly. Yes it has to be studied and implemented carefully, but to make blanket statements about screening as a whole is factually incorrect.

I have not stated "all screening is bad".

Broad-based population screenings as the parent comment suggests, in my opinion, are.

I'm yet to see any clinically-valid distinguishing aspects that would suggest AI would add value to screening. Curious to hear evidence that drives your optimism of human+AI.

Just to state, the NELSON study [1] focuses on high-risk segments. Their paper also recommends a "personalized risk-based approach" to screening. This seems reasonable.

[1] https://www.nejm.org/doi/full/10.1056/nejmoa1911793

The general thread here is about AI helping with a more proactive approach to medicine. Screening for high risk populations certainly falls under that.

You certainly said that screening leads to over diagnosis.

I think for screening, the best results are probably the upcoming prospective study from Kheiron.

https://www.kheironmed.com/news/press-release-new-results-sh...

I suspect, btw, that the Google model in this paper https://www.nature.com/articles/s41586-019-1799-6

will show stronger performance. But Kheiron appears to be ahead as far as proving the value of the tool since they have actually validated prospectively.