aceGenerative AI versus physicians in diagnostic radiology: a systematic review and meta-analysis
Purpose Generative artificial intelligence (AI) models are increasingly evaluated for diagnostic tasks in radiology, yet accuracy, study designs, endpoints, and comparators vary widely. The purpose was to synthesize diagnostic accuracy of generative AI for radiology and compare performance with physicians. Materials and methods A systematic review and meta-analysis was prospectively registered in PROSPERO (CRD420251040000) and conducted in accordance with PRISMA-DTA guidance. Searches of Medline, Scopus, Web of…
Generative AI achieved higher diagnostic accuracy when given text-only input compared to image-only input in radiology tasks.
Generative AI showed significantly lower diagnostic accuracy than expert physicians on radiology tasks, with a 13.0 percentage point gap.
Observed differences in accuracy by input modality may be confounded by task difficulty and information content, and evaluations lacked standardization.
Evidence
- Peer-reviewedJapanese Journal of Radiology2026-10-03
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Truvace Impact Record TRV-2026-1284, v1: “Generative AI versus physicians in diagnostic radiology: a systematic review and meta-analysis.” Truvace, 2026-10-05. /record/TRV-2026-1284 (accessed at citation time). sha256 c33d7889faa59ffb…
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