vida: extract claims from 2026-03-22-nature-medicine-llm-sociodemographic-bias

- Source: inbox/queue/2026-03-22-nature-medicine-llm-sociodemographic-bias.md
- Domain: health
- Claims: 2, Entities: 0
- Enrichments: 3
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>

domains/health/clinical-ai-bias-amplification-creates-compounding-disparity-risk-at-scale.md

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+---
+type: claim
+domain: health
+description: When AI systems designed to support rather than replace physician judgment operate at 30M+ monthly consultations, they systematically amplify rather than reduce healthcare disparities
+confidence: experimental
+source: "Nature Medicine 2025 LLM bias study combined with OpenEvidence adoption data showing 40% US physician penetration"
+created: 2026-04-04
+title: Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities
+agent: vida
+scope: causal
+sourcer: Nature Medicine / Multi-institution research team
+related_claims: ["[[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]]", "[[SDOH interventions show strong ROI but adoption stalls because Z-code documentation remains below 3 percent and no operational infrastructure connects screening to action]]", "[[healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software]]"]
+---
+
+# Clinical AI that reinforces physician plans amplifies existing demographic biases at population scale because both physician behavior and LLM training data encode historical inequities
+
+The Nature Medicine finding that LLMs exhibit systematic sociodemographic bias across all model types creates a specific safety concern for clinical AI systems designed to 'reinforce physician plans' rather than replace physician judgment. Research on physician behavior already documents demographic biases in clinical decision-making. When an AI system trained on historical healthcare data (which reflects those same biases) is deployed to support physicians (who carry those biases), the result is bias amplification rather than correction. At OpenEvidence's scale (40% of US physicians, 30M+ monthly consultations), this creates a compounding disparity mechanism: each AI-reinforced decision that encodes demographic bias becomes training data for future models, creating a feedback loop. The 6-7x LGBTQIA+ mental health referral rate and income-stratified imaging access patterns demonstrate this is not subtle statistical noise but clinically significant disparity. The mechanism is distinct from simple automation bias because the AI is not making errors — it is accurately reproducing patterns from training data that themselves encode inequitable historical practices.

domains/health/llm-clinical-recommendations-exhibit-systematic-sociodemographic-bias-across-all-model-architectures.md

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+---
+type: claim
+domain: health
+description: Analysis of 1.7M outputs from 9 LLMs shows demographic framing alone (race, income, LGBTQIA+ status, housing) alters clinical recommendations when all other case details remain constant
+confidence: likely
+source: Nature Medicine 2025 (PubMed 40195448), multi-institution research team analyzing 1,000 ED cases with 32 demographic variations each
+created: 2026-04-04
+title: LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities
+agent: vida
+scope: causal
+sourcer: Nature Medicine / Multi-institution research team
+related_claims: ["[[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]]", "[[medical LLM benchmark performance does not translate to clinical impact because physicians with and without AI access achieve similar diagnostic accuracy in randomized trials]]", "[[OpenEvidence became the fastest-adopted clinical technology in history reaching 40 percent of US physicians daily within two years]]"]
+---
+
+# LLM clinical recommendations exhibit systematic sociodemographic bias across all model architectures because training data encodes historical healthcare inequities
+
+A Nature Medicine study evaluated 9 LLMs (both proprietary and open-source) using 1,000 emergency department cases presented in 32 sociodemographic variations while holding all clinical details constant. Across 1.7 million model-generated outputs, systematic bias appeared universally: Black, unhoused, and LGBTQIA+ patients received more frequent recommendations for urgent care, invasive interventions, and mental health evaluations. LGBTQIA+ subgroups received mental health assessments approximately 6-7 times more often than clinically indicated. High-income cases received significantly more advanced imaging recommendations (CT/MRI, P < 0.001) while low/middle-income cases were limited to basic or no testing. The critical finding is that bias appeared consistently across both proprietary AND open-source models, indicating this is a structural problem with LLM training data reflecting historical healthcare inequities, not an artifact of any single system's architecture or RLHF approach. The authors note bias magnitude was 'not supported by clinical reasoning or guidelines' — these are model-driven disparities, not acceptable clinical variation.