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LLMs simulate plausible patients but fail to represent real populations

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv reveals that large language models, when tasked with simulating mental health patients, produce individually plausible cases but fail to represent realistic populations. Models like GPT-4o mini, Gemini 3 Flash, DeepSeek-V3, and GLM-4.7 were tested on their ability to generate patient profiles based on demographic cohorts. While individual patient simulations largely adhered to diagnostic criteria, the aggregated populations showed significant deviations, including inflated symptom scores, distorted demographic disparities, and unstable regeneration of patient data. The researchers termed this discrepancy the "coherence-fidelity dissociation." AI

IMPACT Highlights potential risks in using LLMs for mental health simulations due to population-level inaccuracies.

RANK_REASON The cluster contains a research paper detailing findings on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs simulate plausible patients but fail to represent real populations

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The cluster contains a research paper detailing findings on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Patrick Keough ·

    Plausible Patients, Impossible Populations: Auditing Epidemiological Fidelity in Large Language Model Mental Health Simulations

    arXiv:2604.17359v2 Announce Type: replace-cross 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 de…

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