Design and Implementation of a Psychiatry Resident Training System Based on Large Language Models
Project Overview
The document explores the transformative role of generative AI in education, particularly through the development of an AI-based training system designed for psychiatrists. This system integrates large language models, knowledge graphs, and expert systems to enhance the training process by generating realistic case scenarios, facilitating doctor-patient dialogues, and supporting diagnostic decision-making. Clinical trials demonstrated marked improvements in training outcomes, revealing enhanced diagnostic skills and increased user satisfaction among participants. These findings underscore the potential of AI technologies to effectively address existing challenges in psychiatric education, showcasing how generative AI can not only augment learning experiences but also contribute to the overall advancement of professional training in healthcare.
Key Applications
AI-based training system for psychiatrists
Context: Training psychiatrists at various levels in clinical settings
Implementation: Developed using Vue.js and Node.js, incorporating deep learning algorithms and a B/S architecture
Outcomes: System stability reached 99.95%, AI dialogue accuracy of 96.5%, diagnostic accuracy of 92.5%, and user satisfaction of 92.3%. Doctors improved knowledge mastery by 35.6%, clinical thinking by 28.4%, and diagnostic skills by 23.7%.
Challenges: Limited generalization capabilities of models, technical issues with complex cases, high deployment costs, and low user acceptance.
Implementation Barriers
Technical Barrier
Models have limited generalization capabilities and struggle with complex or rare cases.
Proposed Solutions: Further development of machine learning algorithms to enhance generalization capabilities and adaptability.
Adoption Barrier
High deployment costs and low user acceptance hinder widespread implementation.
Proposed Solutions: Promote low-cost alternatives and ensure user-friendly interfaces to improve acceptance.
Project Team
Zhenguang Zhong
Researcher
Jia Tang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zhenguang Zhong, Jia Tang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai