Core Insights - The article discusses a new training framework for AI diagnostic agents, emphasizing the need for dynamic decision-making in clinical diagnosis rather than relying on static data [2][6][10]. Group 1: Framework and Model Development - A novel "Environment-Agent" training framework has been proposed, which includes the creation of a medical diagnostic world model called DiagGym, designed to train self-evolving diagnostic agents known as DiagAgent [2][10]. - DiagGym simulates a virtual clinical environment where diagnostic agents can interact with virtual patients, allowing them to refine their decision-making strategies through continuous feedback [10][14]. - The framework incorporates a comprehensive evaluation benchmark called DiagBench, which consists of 750 cases and 973 detailed assessment criteria developed by physicians to evaluate the diagnostic reasoning process [2][12]. Group 2: Training and Evaluation - The training of DiagAgent involves two main phases: supervised fine-tuning using real clinical interaction data and reinforcement learning in the DiagGym environment to enhance decision-making capabilities [19][15]. - Experimental results indicate that DiagAgent significantly outperforms other advanced models like DeepSeek and Claude-4 in multi-step diagnostic decision-making [12][25]. - The evaluation metrics include diagnostic accuracy, quality of examination recommendations, and efficiency in completing diagnoses, with DiagAgent showing a 44.03% improvement in recommendation hit rate and a 9.34% increase in final diagnosis accuracy compared to other models [25][28]. Group 3: Research Value and Future Prospects - The research aligns AI diagnostics more closely with real clinical workflows by transitioning from static question-answering to dynamic strategy learning, enabling agents to actively gather evidence and make assessments [36][41]. - Future expansions may include integrating treatment plans and prognostic evaluations into the virtual environment, aiming to create a comprehensive diagnostic and treatment AI system [38][40]. - The DiagGym model can be enhanced by incorporating additional dimensions such as treatment feedback and cost/safety constraints, leading to a more holistic virtual clinical system [40][41].