AI Medical Imaging Diagnosis — Specialist-Level Accuracy on Defined Tasks

The claim's surface assertion — specialist-level accuracy on defined imaging tasks — is confirmed on curated research datasets across multiple imaging domains and by regulatory validation in prospective settings for specific cleared devices. The surface layer is advancing: AI medical imaging achieves specialist-level performance on well-defined tasks under controlled conditions. The surface claim is in ESCALATING territory. The claim fragments at the depth layer — specifically, at the boundary between research-dataset accuracy and real-world clinical deployment. Systematic deployment-gap studies (INST-003) document that accuracy measured on curated, single-site datasets does not reliably generalise across scanners, acquisition protocols, or patient demographics, and prospective trials (INST-004) show a heterogeneous picture — some deployed systems retain specialist-level accuracy, others do not. The pressure state is FRAGMENTING: the surface claim is confirmed and advancing, but the depth question — whether research-dataset accuracy is a valid proxy for clinical deployment accuracy — remains open (BN-001), pending further validation of the foundation-model generalisation trend (INST-005).