vida: extract claims from 2026-xx-npj-digital-medicine-current-challenges-regulatory-databases-aimd

- Source: inbox/queue/2026-xx-npj-digital-medicine-current-challenges-regulatory-databases-aimd.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 2
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/regulatory-databases-lack-ai-specific-fields-making-ai-contribution-to-clinical-harm-systematically-unidentifiable.md

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+---
+type: claim
+domain: health
+description: MAUDE and equivalent databases were designed for hardware devices and cannot capture whether AI contributed to harm, what the AI recommended, or how clinicians interacted with outputs
+confidence: experimental
+source: npj Digital Medicine 2026, regulatory database analysis across US/EU/UK systems
+created: 2026-04-02
+title: Regulatory databases lack AI-specific fields making AI contribution to clinical harm systematically unidentifiable from adverse event reports
+agent: vida
+scope: structural
+sourcer: npj Digital Medicine authors
+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]]"]
+---
+
+# Regulatory databases lack AI-specific fields making AI contribution to clinical harm systematically unidentifiable from adverse event reports
+
+The fundamental architecture problem is that regulatory databases (MAUDE, EUDAMED, MHRA) were designed for static hardware devices and lack fields for capturing AI-specific failure modes. When a patient is harmed in a clinical encounter involving an AI tool, the reporting mechanism cannot capture: (1) whether the AI contributed to the harm, (2) what the AI actually recommended, or (3) how the clinician interacted with the AI output. This is not a data quality problem fixable with better reporting compliance—it is a structural limitation where the 'contribution' of AI to harm is categorically unidentifiable from existing report formats. The authors identify this as one of four key challenges, noting that 'attribution problems' mean the causal role of AI in adverse events is invisible to post-market surveillance systems. This creates a systematic blind spot where AI-related harms can occur without generating detectable signals in the regulatory infrastructure designed to catch them.

domains/health/us-eu-uk-regulatory-databases-use-incompatible-ai-classification-systems-making-cross-national-surveillance-structurally-impossible.md

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+---
+type: claim
+domain: health
+description: MAUDE EUDAMED and MHRA use different AI device classification frameworks preventing coordinated monitoring even if individual systems improve
+confidence: experimental
+source: npj Digital Medicine 2026, comparative analysis of three major regulatory database systems
+created: 2026-04-02
+title: US EU and UK regulatory databases use incompatible AI classification systems making cross-national surveillance of globally deployed clinical AI structurally impossible
+agent: vida
+scope: structural
+sourcer: npj Digital Medicine authors
+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]]"]
+---
+
+# US EU and UK regulatory databases use incompatible AI classification systems making cross-national surveillance of globally deployed clinical AI structurally impossible
+
+The three major AI medical device market jurisdictions (US MAUDE, EU EUDAMED, UK MHRA) each maintain separate regulatory databases that use incompatible classification systems for AI devices. This 'global fragmentation' means that even if each individual system were improved to better capture AI-specific adverse events, cross-national surveillance would remain structurally impossible. The same AI tool deployed simultaneously across all three jurisdictions generates adverse event data in three non-interoperable formats. The authors identify this as a critical challenge requiring 'global stakeholders must come together and align efforts to develop a clear roadmap.' The timing is significant: this call for international coordination is published in January 2026, the same quarter as FDA expanded enforcement discretion (January 2026) and EU rolled back high-risk AI requirements (December 2025)—the opposite direction from the recommended coordination.