teleo-codex/domains/health/clinical-ai-safety-gap-is-doubly-structural-with-no-pre-deployment-requirements-and-no-post-market-surveillance.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	related_claims
claim	health	No point in the deployment lifecycle systematically evaluates AI safety for most clinical decision support tools	experimental	Babic et al. 2025 (MAUDE analysis) + FDA CDS Guidance January 2026 (enforcement discretion expansion)	2026-04-02	The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm	vida	structural	Babic et al.	healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs

The clinical AI safety gap is doubly structural: FDA enforcement discretion removes pre-deployment safety requirements while MAUDE's lack of AI-specific fields means post-market surveillance cannot detect AI-attributable harm

The clinical AI safety vacuum operates at both ends of the deployment lifecycle. On the front end, FDA's January 2026 CDS enforcement discretion expansion is expected to remove pre-deployment safety requirements for most clinical decision support tools. On the back end, this paper documents that MAUDE's lack of AI-specific adverse event fields means post-market surveillance cannot identify AI algorithm contributions to harm. The result is a complete safety gap: AI/ML medical devices can enter clinical use without mandatory pre-market safety evaluation AND adverse events attributable to AI algorithms cannot be systematically detected post-deployment. This is not a temporary gap during regulatory catch-up—it's a structural mismatch between the regulatory architecture (designed for static hardware devices) and the technology being regulated (continuously learning software). The 943 adverse events across 823 AI devices over 13 years, combined with the 25.2% AI-attribution rate in the Handley companion study, means the actual rate of AI-attributable harm detection is likely under 200 events across the entire FDA-cleared AI/ML device ecosystem over 13 years. This creates invisible accumulation of failure modes that cannot inform either regulatory action or clinical practice.