Large Language Models for Psychiatric Diagnosis Based on Multicenter Real-World Clinical Records: Comparative Study.

ABSTRACT:

Background

Psychiatric disorders are diagnostically challenging and often rely on subjective clinical judgment, particularly in resource-limited settings. Large language models (LLMs) have demonstrated potential in supporting psychiatric diagnosis; however, robust evidence from large-scale, real-world clinical data remains limited.

Objective

This study aimed to evaluate and compare the diagnostic performance of multiple LLMs for psychiatric disorders using multicenter real-world electronic health records (EHRs).

Methods

We retrospectively analyzed 9923 inpatient EHRs collected from 6 psychiatric centers across China, encompassing all ICD-10 (International Statistical Classification of Diseases, Tenth Revision) psychiatric categories. In total, 3 LLMs-GPT-4.0 (OpenAI),

SUBMITTER: Sun M 

PROVIDER: S-EPMC12848494 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature