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Revolutionizing clinical skills training with Generative AI: the Intelligent Inquiry Training System framework
Hongtao Liu1, Huihui Cheng2, Yun Zhang3
1School of Public Health and Health Management, Guangxi Health Science College, Nanning, China.
Background:
Practical training bridges theoretical knowledge and clinical skills in medical education but faces challenges such as resource scarcity, complex patient-physician relationship, and limited interactivity in traditional systems. Generative artificial intelligence (LLMs) offers innovative solutions for simulating virtual patients and enhancing communication training.
Method:
An Intelligent Inquiry Training System (IITS) was developed using Baidu's Wenxin LLM, featuring dynamic case generation, multimodal examination simulation, and emotional feedback modules. Integrated into a blended surgical curriculum, IITS employed pre-class problem-based learning (PBL) and post-class extended training. System efficacy was evaluated via student/instructor questionnaires.
Results:
IITS received software copyright certification and demonstrated cross-platform compatibility. Student feedback highlighted its auxiliary role, prompt responses, and need for improved diagnostic accuracy. Teachers praised its case generation efficiency and recommended AI-powered evaluation.
Conclusion:
IITS enhances clinical reasoning and communication skills through realistic virtual training, reducing teacher workload. Future iterations will integrate AI scoring and simulator mannequins to validate long-term impacts on clinical competency.
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