Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models

Abstract: Mental disorders have become a significant global public health issue, while the shortage of psychiatrists and inefficient training systems severely hinder the accessibility of mental health services. This paper designs and implements an artificial intelligence-based training system for psychiatrists. By integrating technologies such as LLMs, knowledge graphs, and expert systems, the system constructs an intelligent and standardized training platform. It includes six functional modules: case generation, consultation dialogue, examination prescription, diagnostic decision-making, integrated traditional Chinese and Western medicine prescription, and expert evaluation, providing comprehensive support from clinical skill training to professional level assessment.The system adopts a B/S architecture, developed using the Vue.js and Node.js technology stack, and innovatively applies deep learning algorithms for case generation and doctor-patient dialogue. In a clinical trial involving 60 psychiatrists at different levels, the system demonstrated excellent performance and training outcomes: system stability reached 99.95%, AI dialogue accuracy achieved 96.5%, diagnostic accuracy reached 92.5%, and user satisfaction scored 92.3%. Experimental data showed that doctors using the system improved their knowledge mastery, clinical thinking, and diagnostic skills by 35.6%, 28.4%, and 23.7%, respectively.The research results provide an innovative solution for improving the efficiency of psychiatrist training and hold significant importance for promoting the standardization and scalability of mental health professional development.