{"id":"0bc2a748-27c1-4ca5-b00e-6ac52274e961","arxiv_id":"1908.00381","paper_version":2,"verdict":"UNVERDICTED","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"low","formal_verification":"none","parameter_count":1,"one_line_summary":"The paper sets out a staged framework for clinical validation of AI radiology software using standard diagnostic metrics, reference dataset requirements, and a STARD-based reporting checklist.","lead":"This document is a methodological guideline from the Moscow Health Care Department for running clinical tests and clinical acceptance of AI-based radiology software. It is a practical manual for developers, regulators, and radiologists, not a report of new research findings.","discovery_kind":"review","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-14T15:59:13.427420+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}