用AI重建发表的医疗仿真模型，验证其可复现性。
Unlocking the Potential of Past Research: Using Generative AI to Reconstruct Healthcare Simulation Models
- 基于论文描述用生成式AI重建开源仿真模型
- 成功复现一个模型，另一个因分布信息缺失失败
- 适合关注模型可复现性与AI辅助建模的研究者
离散事件仿真（DES）在医疗运筹学中广泛应用，但模型很少共享，限制了其复用和长期影响。本研究探索使用生成式人工智能（AI）基于学术期刊中的描述，通过自由开源软件（FOSS）重建已发表的仿真模型。采用结构化方法，我们成功生成、测试并内部复现了两个复杂度较高的DES模型，包括用户界面。其中一个模型的结果被成功复现，另一个未复现，可能由于缺少分布信息。这些模型远比此前公开的AI生成模型更复杂。尽管在提示工程、代码生成和模型测试方面面临挑战，但通过迭代开发、系统性对比与测试，以及团队专业知识，重建工作取得成功。
原文摘要 · Abstract (English)
Discrete-event simulation (DES) is widely used in healthcare Operations Research, but the models themselves are rarely shared. This limits their potential for reuse and long-term impact in the modelling and healthcare communities. This study explores the feasibility of using generative artificial intelligence (AI) to recreate published models using Free and Open Source Software (FOSS), based on the descriptions provided in an academic journal. Using a structured methodology, we successfully generated, tested and internally reproduced two DES models, including user interfaces. The reported results were replicated for one model, but not the other, likely due to missing information on distributions. These models are substantially more complex than AI-generated DES models published to date. Given the challenges we faced in prompt engineering, code generation, and model testing, we conclude that our iterative approach to model development, systematic comparison and testing, and the expertise of our team were necessary to the success of our recreated simulation models.
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