Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910798263091200 |
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| author | Zhong, Zhenguang Tang, Jia |
| author_facet | Zhong, Zhenguang Tang, Jia |
| contents | 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 large language models, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models Zhong, Zhenguang Tang, Jia Computers and Society Human-Computer Interaction Neurons and Cognition 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 large language models, 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. |
| title | Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models |
| topic | Computers and Society Human-Computer Interaction Neurons and Cognition |
| url | https://arxiv.org/abs/2501.14530 |