Abstract

Mental disorders affect nearly one billion individuals worldwide, yet professional psychiatric care remains constrained by workforce shortages and experience-dependent decision-making. Despite recent advances in large language models (LLMs), current applications in mental health are primarily patient-oriented and lack alignment with real-world psychiatric clinical workflows. Here we present PsychFound, a domain-adapted and clinician-oriented LLM developed to support psychiatric clinical practice. Developed through a three-phase framework using expert-curated psychiatric corpora and 64,588 Chinese real-world electronic health records, PsychFound integrates psychiatric professional knowledge, clinical reasoning capabilities and adaptation to the full spectrum of psychiatric clinical tasks across diagnosis, treatment planning and longitudinal management in Chinese clinical settings. In retrospective evaluations spanning three professional knowledge assessments and five clinical task benchmarks, the 7B-parameter PsychFound delivered the top overall performance among 22 LLMs. In a real-world, two-arm prospective study, resident psychiatrists assisted by PsychFound demonstrated higher consultation quality, higher diagnostic accuracy, more appropriate medication selection and reduced documentation time (all P < 0.01). A reader study with 60 psychiatrists (20 residents, 20 attendings and 20 seniors) showed that PsychFound’s clinical reasoning performance matched that of attending psychiatrists. These findings demonstrated that PsychFound provides an interpretable, expert-level decision support tool capable of improving consistency, efficiency and standardization in psychiatric clinical care.

Fig. 1: Overview of PsychFound: data foundations, model development pipeline and clinical integration.

Fig. 2: Comprehensive evaluation framework for PsychFound.

https://doi.org/10.1038/s42256-026-01224-w