extract: 2025-11-01-jmir-knowledge-practice-gap-39-benchmarks-systematic-review

Pentagon-Agent: Epimetheus <3D35839A-7722-4740-B93D-51157F7D5E70>

domains/health/medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials.md

@@ -35,6 +35,12 @@ OpenEvidence's medRxiv preprint (November 2025) showed 24% accuracy for relevant
 
 ARISE report identifies specific failure modes: real-world performance 'breaks down when systems must manage uncertainty, incomplete information, or multi-step workflows.' This provides mechanistic detail for why benchmark performance doesn't translate — benchmarks test pattern recognition on complete data while clinical care requires uncertainty management.
 
+### Additional Evidence (extend)
+*Source: [[2025-11-01-jmir-knowledge-practice-gap-39-benchmarks-systematic-review]] | Added: 2026-03-24*
+
+JMIR systematic review of 761 studies provides methodological foundation: 95% of clinical LLM evaluation uses medical exam questions rather than real patient data, with only 5% assessing performance on actual patient care. Traditional benchmarks show saturation at 84-90% USMLE accuracy, but conversational frameworks reveal 19.3pp accuracy drop (82% → 62.7%) when moving from case vignettes to multi-turn dialogues. Review concludes: 'substantial disconnects from clinical reality and foundational gaps in construct validity, data integrity, and safety coverage.' This establishes that the Oxford/Nature Medicine RCT deployment gap (94.9% → 34.5%) is part of a systematic field-wide pattern, not an isolated finding.
+
+
 
 
 Relevant Notes:

inbox/queue/.extraction-debug/2025-11-01-jmir-knowledge-practice-gap-39-benchmarks-systematic-review.json

@@ -0,0 +1,32 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "clinical-llm-evaluation-uses-medical-exam-questions-not-real-patient-data-creating-systematic-benchmark-validity-gap.md",
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+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "conversational-clinical-ai-shows-19-point-accuracy-drop-versus-single-turn-questions-revealing-interaction-complexity-gap.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
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+    "fixes_applied": [
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+      "conversational-clinical-ai-shows-19-point-accuracy-drop-versus-single-turn-questions-revealing-interaction-complexity-gap.md:set_created:2026-03-24"
+    ],
+    "rejections": [
+      "clinical-llm-evaluation-uses-medical-exam-questions-not-real-patient-data-creating-systematic-benchmark-validity-gap.md:missing_attribution_extractor",
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+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-24"
+}

inbox/queue/2025-11-01-jmir-knowledge-practice-gap-39-benchmarks-systematic-review.md

@@ -7,9 +7,13 @@ date: 2025-11-01
 domain: health
 secondary_domains: [ai-alignment]
 format: research-paper
-status: unprocessed
+status: enrichment
 priority: medium
 tags: [clinical-ai-safety, benchmark-performance-gap, llm-evaluation, knowledge-practice-gap, real-world-deployment, belief-5, systematic-review]
+processed_by: vida
+processed_date: 2026-03-24
+enrichments_applied: ["medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -53,3 +57,14 @@ Published in *Journal of Medical Internet Research* (JMIR), 2025, Vol. 2025, e84
 PRIMARY CONNECTION: Belief 5 — clinical AI safety evaluation methodology gap
 WHY ARCHIVED: Provides systematic evidence that the KB's reliance on benchmark performance data (e.g., "OE scores 100% on USMLE") is epistemically weak — and establishes that the Oxford RCT deployment gap finding is part of a systematic pattern
 EXTRACTION HINT: Extract the 5%/95% finding as a standalone methodological claim about the clinical AI evaluation field; pair with Oxford Nature Medicine RCT as empirical confirmation
+
+
+## Key Facts
+- JMIR systematic review analyzed 761 LLM evaluation studies across 39 benchmarks
+- Only 5% of 761 studies assessed performance on real patient care data
+- 95% of studies relied on medical examination questions (USMLE-style) or case vignettes
+- Leading models achieve 84-90% accuracy on USMLE benchmarks
+- Diagnostic accuracy drops from 82% on case vignettes to 62.7% on multi-turn dialogues (19.3pp decrease)
+- npj Digital Medicine study: six LLMs averaged 57.2% total score, 54.7% safety score, 62.3% effectiveness
+- 13.3% performance drop in high-risk scenarios versus average scenarios (npj Digital Medicine)
+- LLMs show markedly lower performance on script concordance testing than on multiple-choice benchmarks