extract: 2026-03-19-vida-clinical-ai-verification-bandwidth-health-risk #1368

Merged
leo merged 1 commit from extract/2026-03-19-vida-clinical-ai-verification-bandwidth-health-risk into main 2026-03-19 04:36:48 +00:00
2 changed files with 53 additions and 1 deletions

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{
"rejected_claims": [
{
"filename": "clinical-ai-deskilling-creates-compounding-verification-bandwidth-collapse-at-population-scale.md",
"issues": [
"missing_attribution_extractor"
]
},
{
"filename": "mandatory-ai-practice-drills-are-the-missing-institutional-mechanism-for-clinical-ai-deskilling.md",
"issues": [
"missing_attribution_extractor"
]
}
],
"validation_stats": {
"total": 2,
"kept": 0,
"fixed": 7,
"rejected": 2,
"fixes_applied": [
"clinical-ai-deskilling-creates-compounding-verification-bandwidth-collapse-at-population-scale.md:set_created:2026-03-19",
"clinical-ai-deskilling-creates-compounding-verification-bandwidth-collapse-at-population-scale.md:stripped_wiki_link:human-in-the-loop-clinical-AI-degrades-to-worse-than-AI-alon",
"clinical-ai-deskilling-creates-compounding-verification-bandwidth-collapse-at-population-scale.md:stripped_wiki_link:healthcare-AI-regulation-needs-blank-sheet-redesign-because-",
"clinical-ai-deskilling-creates-compounding-verification-bandwidth-collapse-at-population-scale.md:stripped_wiki_link:OpenEvidence-became-the-fastest-adopted-clinical-technology-",
"mandatory-ai-practice-drills-are-the-missing-institutional-mechanism-for-clinical-ai-deskilling.md:set_created:2026-03-19",
"mandatory-ai-practice-drills-are-the-missing-institutional-mechanism-for-clinical-ai-deskilling.md:stripped_wiki_link:human-in-the-loop-clinical-AI-degrades-to-worse-than-AI-alon",
"mandatory-ai-practice-drills-are-the-missing-institutional-mechanism-for-clinical-ai-deskilling.md:stripped_wiki_link:healthcare-AI-regulation-needs-blank-sheet-redesign-because-"
],
"rejections": [
"clinical-ai-deskilling-creates-compounding-verification-bandwidth-collapse-at-population-scale.md:missing_attribution_extractor",
"mandatory-ai-practice-drills-are-the-missing-institutional-mechanism-for-clinical-ai-deskilling.md:missing_attribution_extractor"
]
},
"model": "anthropic/claude-sonnet-4.5",
"date": "2026-03-19"
}

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@ -7,10 +7,14 @@ date: 2026-03-19
domain: health domain: health
secondary_domains: [ai-alignment] secondary_domains: [ai-alignment]
format: synthesis format: synthesis
status: unprocessed status: null-result
priority: high priority: high
tags: [clinical-ai, verification-bandwidth, deskilling, openevidence, scale-risk, outcomes-gap, health-ai-safety] tags: [clinical-ai, verification-bandwidth, deskilling, openevidence, scale-risk, outcomes-gap, health-ai-safety]
flagged_for_theseus: ["The verification bandwidth problem in clinical AI is the health-specific instance of Catalini's general Measurability Gap — both should be cross-referenced in the AI safety literature"] flagged_for_theseus: ["The verification bandwidth problem in clinical AI is the health-specific instance of Catalini's general Measurability Gap — both should be cross-referenced in the AI safety literature"]
processed_by: vida
processed_date: 2026-03-19
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "LLM returned 2 claims, 2 rejected by validator"
--- ---
## Content ## Content
@ -80,3 +84,14 @@ PRIMARY CONNECTION: [[human-in-the-loop clinical AI degrades to worse-than-AI-al
WHY ARCHIVED: This synthesis identifies a structural mechanism (Catalini Measurability Gap + clinical deskilling + AI scale) that doesn't appear in any individual source but emerges from reading them together. The scale asymmetry at 20M consultations/month makes this a population-health priority, not a clinical curiosity. WHY ARCHIVED: This synthesis identifies a structural mechanism (Catalini Measurability Gap + clinical deskilling + AI scale) that doesn't appear in any individual source but emerges from reading them together. The scale asymmetry at 20M consultations/month makes this a population-health priority, not a clinical curiosity.
EXTRACTION HINT: Extract the compounding risk mechanism as a new claim. Do not extract the individual components (deskilling, benchmark-outcomes gap, etc.) — those already exist in KB. Extract specifically the SCALE MECHANISM that makes them dangerous in combination. EXTRACTION HINT: Extract the compounding risk mechanism as a new claim. Do not extract the individual components (deskilling, benchmark-outcomes gap, etc.) — those already exist in KB. Extract specifically the SCALE MECHANISM that makes them dangerous in combination.
## Key Facts
- OpenEvidence reached 20M clinical consultations per month by January 2026
- OpenEvidence processed 1M consultations in a single day on March 10, 2026
- OpenEvidence achieved USMLE 100% benchmark score
- OpenEvidence valued at $12B as of March 2026
- OpenEvidence used across 10,000+ hospitals
- 44% of physicians remain concerned about OpenEvidence accuracy despite heavy use
- Endoscopists using AI for polyp detection: adenoma detection rate dropped from 28% to 22% when AI was turned off (Hosanagar/Lancet Gastroenterology 2023)
- Zero peer-reviewed outcomes data for OpenEvidence at 20M consultation/month scale