Not all of that is out of reach. Making the AI evaluate a paper in the context of a cluster of related papers might enable spotting some "too good to be true" things.
Hey, here's an idea: use AI for mapping out the influence of papers that were later retracted (whether for fraud or error, it doesn't matter). Not just via citation, but have it try to identify the no longer supported conclusions from a retracted paper, and see where they show up in downstream papers. (Cheap "downstream" is when a paper or a paper in a family of papers by the same team ever cited the upstream paper, even in preprints. More expensive downstream is doing it without citations.)