fix: strip code fences from Babic MAUDE AI extraction frontmatter

Original extraction (PR #2257) wrapped YAML frontmatter in code blocks.
Stripped code fences, added proper --- delimiters. Content unchanged.

Co-Authored-By: Epimetheus <noreply@teleohq.com>

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

@@ -1,4 +1,4 @@
-```yaml
+---
 type: claim
 domain: health
 description: No point in the deployment lifecycle systematically evaluates AI safety for most clinical decision support tools
@@ -15,4 +15,3 @@ related_claims: ["[[healthcare AI regulation needs blank-sheet redesign because
 # 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.
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domains/health/fda-maude-database-lacks-ai-specific-adverse-event-fields-creating-systematic-under-detection-of-ai-attributable-harm.md

@@ -1,4 +1,3 @@
-```markdown
 ---
 type: claim
 domain: health
@@ -16,4 +15,3 @@ related_claims: ["[[human-in-the-loop clinical AI degrades to worse-than-AI-alon
 # FDA's MAUDE database systematically under-detects AI-attributable harm because it has no mechanism for identifying AI algorithm contributions to adverse events
 
 MAUDE recorded only 943 adverse events across 823 FDA-cleared AI/ML devices from 2010-2023—an average of 0.76 events per device over 13 years. For comparison, FDA reviewed over 1.7 million MDRs for all devices in 2023 alone. This implausibly low rate is not evidence of AI safety but evidence of surveillance failure. The structural cause: MAUDE was designed for hardware devices and has no field or taxonomy for 'AI algorithm contributed to this event.' Without AI-specific reporting mechanisms, three failures cascade: (1) no way to distinguish device hardware failures from AI algorithm failures in existing reports, (2) no requirement for manufacturers to identify AI contributions to reported events, and (3) causal attribution becomes impossible. The companion Handley et al. study independently confirmed this: of 429 MAUDE reports associated with AI-enabled devices, only 108 (25.2%) were potentially AI/ML related, with 148 (34.5%) containing insufficient information to determine AI contribution. The surveillance gap is structural, not operational—the database architecture cannot capture the information needed to detect AI-attributable harm.
-```