Revolutionizing clinical skills training with Generative AI: the Intelligent Inquiry Training System framework

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.