There's probably 10 X more problematic academic publications, than currently get flagged. Automating the search for the likeliest candidates is going to be very helpful by focusing the "critical eye" where it can make the biggest difference.

The largest problems with most publications (in epi and in my opinion at least) is study design. Unfortunately, faulty study design or things like data cleaning is qualitative, nuanced, and difficult to catch with AI unless it has access to the source data.

I think some people will find an advantage in flagging untold numbers of research papers as frivolous or fraudulent with minimal effort, while putting the burden of re-proving the work on everyone else.

In other words, I fear this is a leap in Gish Gallop technology.

Hopefully, one would use this to try to find errors in a massive number of papers, and then go through the effort of reviewing these papers themselves before bringing up the issue. It makes no sense to put effort unto others just because the AI said so.

If reviewer effort is limited and the model has at least a bias in the right direction.

So I just need to make sure my fraud goes under the radar of these AI tools, and then the limited reviewer effort will be spent elsewhere.