Plausible Patients, Impossible Populations: Auditing Epidemiological Fidelity in Large Language Model Mental Health Simulations
arXiv:2604.17359v2 Announce Type: replace Abstract: Language models asked to simulate psychiatric patients produce cases that survive inspection one at a time and populations that match no real one. We gave GPT-4o-mini, Gemini-3-Flash, DeepSeek-V3 and GLM-4.7 each of 120 demographic cohorts under two framings, one written as a clinician enters a patient and one as a person describes themselves, and scored all 28,800 responses against survey-weighted PHQ-8 anchors derived from NHANES microdata. C
Record details
Published: 7 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Healthcare · Transparency
Retrieved: 7 August 2026
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ethics.ai (7 August 2026), “Plausible Patients, Impossible Populations: Auditing Epidemiological Fidelity in Large Language Model Mental Health Simulations,” evidence record 17023, https://ethics.ai/record/17023 (originally published by arXiv cs.CY).
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