Background
Accurate and timely mental health diagnosis is crucial for effective treatment. While clinicians traditionally rely on the DSM-5, the CapyDx leverages AI, specifically a large language model (LLM), to streamline and enhance this process. By analyzing detailed client information, including symptoms and demographics, this advanced language model aims to provide more precise and efficient diagnoses, supporting clinicians in delivering optimal care.
Publications
- Wang L, Jiang Y. (2026). Large Language Model–Powered Diagnostic Co-Pilot (“CapyEngine”) for Mental Disorders: Development, Evaluation, and Future Optimization Study. JMIR AI. 2026;5:e70017. https://ai.jmir.org/2026/1/e70017.
Conference Activities
- Wang, L., & Jiang, Y. (2025, March 26-29). CapyEngine: LLM-Based Diagnostic Co-Pilot for Mental Health – Development, Evaluation, and Optimization of an AI Tool for Mental Disorder Diagnosis [Oral Presentation]. Society of Behavioral Medicine 46th Annual Meeting. San Francisco, CA
Funding Source
Florida State University