ASSESSABLE BUT UNASKED: EVIDENCE-GROUNDED CLINICAL REVIEW FOR MEDICAL AI PAPERS

The clinical dimensions that a medical AI (MedAI) paper makes assessable do not fully overlap with those raised as substantive concerns or requests in its peer review. A nine-dimensional diagnostic analysis shows that clinical factors, espe- cially clinical safety, are less frequently made explicit in written review and are not fully represented by a single overall-quality axis. We introduce Evidence-Grounded Clinical Review (EGCR), a claim-level review system that converts clinical claims into evidence questions, retrieves submission- time external evidence, assesses source applicability, and aligns external require- ments with manuscript evidence to produce structured, inspectable findings. We evaluate EGCR on 168 MedAI papers from ICLR and Nature-series journals. For 107 papers, EGCR identifies at least one clinical dimension that it marks as assessable but that is not raised as a substantive concern or request in the held-out human review. Human-review dimension coverage is 73.38%, while the union coverage of human review and EGCR is 89.80%. A traceability audit shows 97.1% manuscript-evidence recoverability, complete preservation of external provenance chains, and 97.5% submission-time source availability. Paired case analysis against direct review further illustrates how evi- dence grounding can produce more specific validation questions and better align evidence requests with the paper’s stated claim scope. EGCR is not an accept/reject predictor; it provides an inspectable clinical evidence layer that makes the basis of review findings easier to trace and examine.