邁向自主且可審計的醫學影像模型開發

摘要

大型语言模型（LLM）智能体正通过耦合规划、代码执行、调试与实证反馈，逐步实现机器学习工程（MLE）的自动化。然而，将这一能力迁移至医学影像领域仍面临挑战，因为每项任务都需要针对特定模态进行实验设计，并对验证协议与预测工件提出严格要求。为此，我们提出AMID——一种面向医学影像模型开发的自主多智能体框架。AMID首先引入数据条件化方法规划（Data-Conditioned Method Planning），该策略将粗粒度的任务级搜索空间细化为可执行、可并行化的方法通道（method lanes），并基于任务特异性数据分析与可运行的医学影像资源进行构建。随后，AMID开发了验证引导的两阶段优化（Verification-Guided Two-Stage Optimization），从广泛探索多样化方法通道的初期阶段，过渡至选择性利用具有潜力的候选方案，同时在整个优化过程中对验证协议、指标计算以及预测工件实施严格的验证约束。在涵盖多种模态与预测类型的20项医学影像挑战任务中，AMID的表现均优于所评估的通用MLE系统，并在若干任务中达到或接近由人类专家精心设计的解决方案水平。这些结果表明，AMID能够将针对特定任务的医学影像模型开发，从定制化手工工程转化为一种智能化工作流，从而在异构任务中生成高性能且可审计的模型工件。

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.