{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T16:46:40Z","timestamp":1791737200229,"version":"4.3.4"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIES"],"abstract":"<jats:p>AI-enabled Software as a Medical Device (AI-SaMD) is increasingly governed through lifecycle instruments such as predetermined change control plans and Good Machine Learning Practice. Federated learning has been advanced as a privacy-preserving alternative to centralized training, but it destabilizes a foundational assumption of medical device regulation: that governance can be attached to a discrete artifact. We argue that, under continuous cross-institutional adaptation, federated AI-SaMD cannot be governed as a model alone because responsibility becomes reconstructable only at institutional coordination boundaries. We develop interface governance as a necessary accountability layer for those boundaries. We make four contributions. We reconceptualize federated AI-SaMD as distributed clinical infrastructure and specify when an ordinary operational interaction becomes a formal governance interface: when clinically consequential risk, evidence, authority, or contestation moves across institutions and cannot be reconstructed from any single actor\u2019s records. We then translate the framework into four minimal implementation conditions: standardized triggers, auditable logs, defined escalation routes, and institution-specific response duties. We also develop a patient-facing pathway for AI-involvement review, disclosure, and institutional response without requiring technical knowledge of federation. Interface governance does not replace existing regulatory mechanisms; it specifies the conditions under which they remain contestable, auditable, and actionable in federated clinical AI.<\/jats:p>","DOI":"10.1609\/aies.v9i1.43147","type":"journal-article","created":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T16:12:50Z","timestamp":1791735170000},"page":"375-383","source":"Crossref","is-referenced-by-count":0,"title":["From Model Approval to Interface Governance: Federated Learning, Lifecycle Accountability, and AI-Enabled Software as a Medical Device"],"prefix":"10.1609","volume":"9","author":[{"given":"Liu","family":"Ben","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,10,11]]},"container-title":["Proceedings of the AAAI\/ACM Conference on AI, Ethics, and Society"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/download\/43147\/50707","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/download\/43147\/50707","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T16:12:51Z","timestamp":1791735171000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AIES\/article\/view\/43147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,11]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,10,11]]}},"URL":"https:\/\/doi.org\/10.1609\/aies.v9i1.43147","relation":{},"ISSN":["3065-8365"],"issn-type":[{"value":"3065-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,11]]}}}