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LLMs show mixed results in psychiatric screening, need validation

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv evaluated the performance of five large language models in psychiatric screening using a benchmark of 555 interviews. The models demonstrated varying accuracy, with GPT-4.1 Mini and GPT-5 Mini showing the most consistent results. Researchers found that LLMs tended to discount symptom evidence when patients reported preserved functioning or social support, highlighting a need for careful validation before clinical use. AI

IMPACT LLMs show potential for scalable psychiatric screening but require careful validation due to biases in evidence interpretation.

RANK_REASON The cluster contains an academic paper detailing research on LLM capabilities and limitations.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Jianfeng Zhu, Megan Korhummel, Ruoming Jin, Karin G. Coifman ·

    When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening

    arXiv:2605.23148v1 Announce Type: new Abstract: As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs) may identify psychiatric risk from patient narratives, but their reliability ac…

  2. arXiv cs.CL TIER_1 English(EN) · Karin G. Coifman ·

    When Symptoms Are Not Enough: Evidence-Weighting Patterns in Large Language Model Psychiatric Screening

    As demand for mental health care outpaces clinician-delivered assessment, scalable screening tools are increasingly needed. Large language models (LLMs) may identify psychiatric risk from patient narratives, but their reliability across diagnoses, demographic subgroups, and evide…

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