让医生用自然语言直接生成临床AI模型，减少沟通成本。
From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Development
- 医生用口语描述任务，系统自动生成并优化代码流水线。
- 在5个临床任务中准确实现医生需求，肺气胸识别中依赖导管的偏差从60%降至31%。
- 适合希望直接参与AI开发的临床专家，降低对技术团队依赖。
开发实用的临床AI模型需要医生与AI开发者高效协作，但当前需反复沟通需求，过程耗时且易产生误解。编码代理可弥补这一鸿沟，具备医学与AI双重知识，能自主编写和优化代码。本文提出一个原型系统，允许医生以自然语言描述任务，系统自动构建可运行的模型流程，通过与医生共同实验迭代，最终输出符合临床目标的模型。在五个临床任务中，系统均可靠实现医生意图并达到竞争性性能。尤为显著的是，在胸部X光片上，模型对胸腔引流管的依赖性从60%降至31%（一数据集），从50%降至18%（另一数据集）。结果表明，编码代理可推动临床AI开发向以医生为主导的方向转变，使领域专家能直接塑造模型，而非通过专业团队转述需求。
原文摘要 · Abstract (English)
Developing AI models that are useful in clinical practice, requires efficient collaboration between clinicians and AI developers. This poses a practical challenge: clinicians must repeatedly communicate and refine their requirements with AI developers before those requirements can be translated into executable model development. This iterative process is time-consuming, and even after repeated discussion, misalignment may still exist because the two sides do not fully share each other's expertise. Coding agents may help close this gap. They can write and refine code on their own, and they carry working knowledge of both medicine and AI to understand commands formulated by both medical experts and developers. We present a prototype that lets clinicians drive AI development directly. A clinician describes the task in plain language, and the system turns the description into a working pipeline, refines it through repeated experiments together with the clinician, and returns a model that meets the stated clinical objective. Across five clinical tasks, the system reliably produces models that matched the clinician's request and reached competitive performance. Most notably, on chest radiographs the system sharply reduced the model's reliance on chest drains, a well-known shortcut for pneumothorax classification, from 60% to 31% on one dataset and from 50% to 18% on another. Our results suggest that coding agents can shift clinical AI development toward a more clinician-driven mode, allowing domain experts to shape models directly instead of relaying requirements through specialized AI teams.
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