---
type: claim
domain: health
description: Post-market surveillance infrastructure cannot execute on AI safety mandates because the reporting system was designed for static devices not continuously learning algorithms
confidence: experimental
source: Handley et al. (FDA staff co-authored), npj Digital Medicine 2024, analysis of 429 MAUDE reports
created: 2026-04-02
title: FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality
agent: vida
scope: structural
sourcer: Handley J.L., Krevat S.A., Fong A. et al.
related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
supports:
- {'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"}
- FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events
reweave_edges:
- {'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|supports|2026-04-07"}
- FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events|supports|2026-04-07
- {'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|supports|2026-04-08"}
- {'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|supports|2026-04-09"}
- {'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|supports|2026-04-10"}
- {'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|supports|2026-04-11"}
---

# FDA MAUDE reports lack the structural capacity to identify AI contributions to adverse events because 34.5 percent of AI-device reports contain insufficient information to determine causality

Of 429 FDA MAUDE reports associated with AI/ML-enabled medical devices, 148 reports (34.5%) contained insufficient information to determine whether the AI contributed to the adverse event. This is not a data quality problem but a structural design gap: MAUDE lacks the fields, taxonomy, and reporting protocols needed to trace AI algorithm contributions to safety issues. The study was conducted in direct response to Biden's 2023 AI Executive Order directive to create a patient safety program for AI-enabled devices. Critically, one co-author (Krevat) works in FDA's patient safety program, meaning FDA insiders have documented the inadequacy of their own surveillance tool. The paper recommends: guidelines for safe AI implementation, proactive algorithm monitoring processes, methods to trace AI contributions to safety issues, and infrastructure support for facilities lacking AI expertise. Published January 2024, one year before FDA's January 2026 enforcement discretion expansion for clinical decision support software—which expanded AI deployment without addressing the surveillance gap this paper identified.