diff --git a/domains/health/human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs.md b/domains/health/human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs.md index ecc958e85..a33239779 100644 --- a/domains/health/human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs.md +++ b/domains/health/human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs.md @@ -38,6 +38,12 @@ OpenEvidence's 1M daily consultations (30M+/month) with 44% of physicians expres The Sutter Health-OpenEvidence EHR integration creates a natural experiment in automation bias: the same tool (OpenEvidence) that was previously used as an external reference is now embedded in primary clinical workflows. Research on in-context vs. external AI shows in-workflow suggestions generate higher adherence, suggesting the integration will increase automation bias independent of model quality changes. +### Additional Evidence (extend) +*Source: [[2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems]] | Added: 2026-03-23* + +Mount Sinai's multi-agent architecture study (npj Health Systems, March 2026) provides the first peer-reviewed clinical demonstration of an alternative architecture that avoids human-in-the-loop degradation. By distributing tasks among specialized agents rather than requiring human oversight of a single generalist model, the system maintains performance under heavy workload where single-agent systems degrade. This suggests the solution to HITL degradation is architectural (multi-agent specialization) rather than training-based (better models) or procedural (better human oversight protocols). + + Relevant Notes: - [[centaur team performance depends on role complementarity not mere human-AI combination]] -- the chess centaur model does NOT generalize to clinical medicine where physician overrides degrade AI performance diff --git a/inbox/queue/.extraction-debug/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.json b/inbox/queue/.extraction-debug/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.json new file mode 100644 index 000000000..b2adab616 --- /dev/null +++ b/inbox/queue/.extraction-debug/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.json @@ -0,0 +1,35 @@ +{ + "rejected_claims": [ + { + "filename": "multi-agent-clinical-ai-reduces-computational-cost-65x-while-maintaining-performance-under-workload.md", + "issues": [ + "missing_attribution_extractor" + ] + }, + { + "filename": "multi-agent-clinical-ai-adoption-driven-by-efficiency-not-safety-creating-accidental-harm-reduction.md", + "issues": [ + "missing_attribution_extractor" + ] + } + ], + "validation_stats": { + "total": 2, + "kept": 0, + "fixed": 5, + "rejected": 2, + "fixes_applied": [ + "multi-agent-clinical-ai-reduces-computational-cost-65x-while-maintaining-performance-under-workload.md:set_created:2026-03-23", + "multi-agent-clinical-ai-reduces-computational-cost-65x-while-maintaining-performance-under-workload.md:stripped_wiki_link:human-in-the-loop clinical AI degrades to worse-than-AI-alon", + "multi-agent-clinical-ai-adoption-driven-by-efficiency-not-safety-creating-accidental-harm-reduction.md:set_created:2026-03-23", + "multi-agent-clinical-ai-adoption-driven-by-efficiency-not-safety-creating-accidental-harm-reduction.md:stripped_wiki_link:human-in-the-loop clinical AI degrades to worse-than-AI-alon", + "multi-agent-clinical-ai-adoption-driven-by-efficiency-not-safety-creating-accidental-harm-reduction.md:stripped_wiki_link:healthcare AI creates a Jevons paradox because adding capaci" + ], + "rejections": [ + "multi-agent-clinical-ai-reduces-computational-cost-65x-while-maintaining-performance-under-workload.md:missing_attribution_extractor", + "multi-agent-clinical-ai-adoption-driven-by-efficiency-not-safety-creating-accidental-harm-reduction.md:missing_attribution_extractor" + ] + }, + "model": "anthropic/claude-sonnet-4.5", + "date": "2026-03-23" +} \ No newline at end of file diff --git a/inbox/queue/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.md b/inbox/queue/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.md index e44baf41e..e1906015b 100644 --- a/inbox/queue/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.md +++ b/inbox/queue/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.md @@ -7,9 +7,13 @@ date: 2026-03-09 domain: health secondary_domains: [ai-alignment] format: research paper -status: unprocessed +status: enrichment priority: high tags: [clinical-ai-safety, multi-agent-ai, efficiency, noharm, agentic-ai, healthcare-workflow, atoms-to-bits, belief-5] +processed_by: vida +processed_date: 2026-03-23 +enrichments_applied: ["human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs.md"] +extraction_model: "anthropic/claude-sonnet-4.5" --- ## Content @@ -58,3 +62,11 @@ Published online March 9, 2026 in npj Health Systems. Senior author: Girish N. N PRIMARY CONNECTION: "human-in-the-loop clinical AI degrades to worse-than-AI-alone" — multi-agent is the architectural counter-proposal; this paper is the first commercial-grade evidence for that architecture WHY ARCHIVED: First peer-reviewed demonstration of multi-agent clinical AI entering healthcare deployment; the framing gap (efficiency vs. safety) is a new KB finding about how research evidence translates to market adoption EXTRACTION HINT: Extract two claims: (1) multi-agent architecture outperforms single-agent on efficiency AND performance in healthcare; (2) multi-agent is being adopted for efficiency reasons not safety reasons, creating a paradoxical situation where NOHARM's safety case may be implemented accidentally via cost-reduction adoption. The second claim requires care — it's an inference, should be "experimental." + + +## Key Facts +- Mount Sinai study evaluated multi-agent AI across patient information retrieval, clinical data extraction, and medication dose checking +- Study published March 9, 2026 in npj Health Systems +- Coverage by EurekAlert!, Medical Xpress, NewsWise, and News-Medical +- Dr. Nathan Moore demonstrated multi-agent for end-of-life and advance care planning automation at HIMSS 2026 Global Health Conference +- BCG published 'AI agents will transform health care in 2026' report in January 2026