迈向自主且可审计的医学影像模型开发

摘要

大语言模型智能体正开始通过耦合规划、代码执行、调试和经验反馈来实现机器学习工程的自动化。然而，将该能力迁移至医学影像领域仍面临困难，因为每项任务都涉及模态特定的实验设置、严格的验证协议要求以及预测产物的准确性约束。本文提出AMID，一个用于医学影像模型开发的自主多智能体框架。AMID首先提出数据条件化方法规划，通过基于任务特定数据分析和可运行的医学影像资源，将粗粒度的任务级搜索空间细化为可执行、可并行化的方法路径。随后，AMID开发了验证引导的两阶段优化策略：从对多样化方法路径的广泛早期探索，转向对候选方案的定向利用，并在整个优化过程中强制实施对验证协议、评估指标计算和预测产物的严格核查。在涵盖多种模态和预测类型的20个医学影像挑战任务中，AMID的表现优于所评估的通用型机器学习工程系统，并在一部分任务中接近甚至达到了高水平人工设计的挑战解决方案。这些结果表明，AMID能够将特定任务的医学影像模型开发从定制化人工工程转变为一种面向异构任务、可产出高性能且可审计模型产物的智能体工作流。

English

Large language model (LLM) agents are beginning to automate machine learning engineering (MLE) by coupling planning, code execution, debugging, and empirical feedback. Translating this capability to medical imaging remains difficult because each task imposes modality-specific experimentation and strict requirements for validation protocols and prediction artifacts. Here we introduce AMID, an autonomous multi-agent framework for medical imaging model development. AMID first proposes Data-Conditioned Method Planning, which refines coarse task-level search spaces into executable, parallelizable method lanes grounded in task-specific data analysis and runnable medical-imaging resources. It then develops Verification-Guided Two-Stage Optimization, moving from broad early exploration of diverse method lanes to selective exploitation of promising candidates while enforcing strict verification of validation protocols, metric computation, and prediction artifacts throughout the optimization. Across 20 medical imaging challenge tasks spanning diverse modalities and prediction types, AMID outperformed evaluated general-purpose MLE systems and, on several tasks, approached or matched strong human-designed challenge solutions. These results suggest that AMID can turn task-specific medical imaging model development from bespoke manual engineering into an agentic workflow for producing high-performing and auditable model artifacts across heterogeneous tasks.