{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T04:50:47Z","timestamp":1767156647383,"version":"build-2065373602"},"reference-count":19,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>We propose Medicine for Artificial Intelligence (MAI), a clinical framework that reconceptualizes AI anomalies as diseases requiring systematic screening, differential diagnosis, treatment, and follow-up. Contemporary discourse on failures (e.g., \u201challucination\u201d) is <jats:italic>ad hoc<\/jats:italic> and fragmented across domains, impeding cumulative knowledge and reproducible management. MAI adapts medical nosology to AI by formalizing core constructs\u2014disease, symptom, diagnosis, treatment, and classification\u2014and mapping a clinical workflow (examination \u2192 diagnosis \u2192 intervention) onto the AI lifecycle. As a proof-of-concept, we developed DSA-1, a prototype taxonomy of 45 disorders across nine functional chapters. This approach clarifies ambiguous failure modes (e.g., distinguishing hallucination subtypes), links diagnoses to actionable interventions and evaluation metrics, and supports lifecycle practices, including triage and \u201cAI health checks.\u201d MAI further maps epidemiology, severity, and detectability to risk-assessment constructs, complementing top-down governance with bottom-up technical resolution. By aligning clinical methodology with AI engineering and coordinating researchers, clinicians, and regulators, MAI offers a reproducible foundation for safer, more resilient, and auditable AI systems.<\/jats:p>","DOI":"10.3389\/frai.2025.1698717","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T05:27:07Z","timestamp":1759987627000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Medicine for artificial intelligence: applying a medical framework to AI anomalies"],"prefix":"10.3389","volume":"8","author":[{"given":"Takahiro","family":"Kato","sequence":"first","affiliation":[]},{"given":"Daisuke","family":"Komura","sequence":"additional","affiliation":[]},{"given":"Binay","family":"Panda","sequence":"additional","affiliation":[]},{"given":"Shumpei","family":"Ishikawa","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"ref1","year":"2025"},{"key":"ref2","author":"Bengio","year":""},{"key":"ref3","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/0167-4048(87)90122-2","article-title":"Computer viruses: theory and experiments","volume":"6","author":"Cohen","year":"1987","journal-title":"Comput. 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