Large Language Model–Powered Diagnostic Co-Pilot (“CapyEngine”) for Mental Disorders: Development, Evaluation, and Future Optimization Study
Overall, ChatGPT-4o achieved the highest accuracy at less stringent benchmarks (top 10 and top 5), while clinician performance did not differ significantly from ChatGPT-4o in identifying the single most likely diagnosis. Although CapyEngine was less accurate overall, it exhibited more consistent and constrained diagnostic ranking across evaluation benchmarks, likely reflecting its DSM-5-TR-based, domain-specific design rather than broader diagnostic coverage. Nonetheless, CapyEngine shows promise as a tool to augment the mental health diagnostic process, and further research is needed to evaluate the risks and benefits of integrating artificial intelligence systems, such as CapyEngine, into clinical workflows.
Authors
- Yunzhang Jiang (ORCID: https://orcid.org/0009-0008-5579-6954)
- Liying Wang
Institutions
- Florida State University (US)
- Nexen (Canada) (CA)
Publication Details
- Journal
- JMIR AI
- Published
- 2026-02-24
- DOI
- https://doi.org/10.2196/70017
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 0.00