Radiology peer review software and AI peer learning that finds the cases worth reviewing

Nearly every radiology group in the country runs peer review, and very few of them think it is working. The mechanics are familiar: a percentage of reads gets pulled for a colleague to score, the scores go into a spreadsheet or a module, the numbers go to the quality committee, and the committee files them because an accreditation body asked for them. The Joint Commission expects documentation at roughly a five percent random sampling rate. Five percent chosen at random, on a distribution where the reads that actually matter are rare, is a sampling method almost perfectly designed to miss the interesting case.

That is the real problem with peer review, and it is why the field has been moving toward peer learning instead. A survey of ACR members found 53 percent of respondents using peer learning, against 29 percent who were not. Scoring a colleague one through four produces a defensible audit trail and almost no education. Peer learning drops the score, keeps the case, and puts it in front of the group as something to learn from. The catch is that peer learning still depends on somebody finding the case in the first place, and random sampling finds it no better than it did before.

This is where Radiological.ai fits, and it is worth being exact about the boundary. We are not a peer review platform. We do not hold your scores, we do not submit to RADPEER, and we do not produce your accreditation documentation. What we do is read every study that comes through, flag the regions that look like a suspected finding, and draft the structured report. When a flag on a study does not line up with what the signed report says, that study is a candidate worth a human look. That is case selection, not adjudication, and every judgment about whether anything was actually missed stays with the radiologists on your quality committee.