AMID: Medical Imaging Modeling Agent
Shengyuan Liu · hf · 2026-07-15
This work introduces AMID, an autonomous multi-agent framework for medical image model development. It attempts to automate the traditionally manual MLE pipeline while meeting the strict validation and auditability requirements of medical scenarios.
Core designs include:
- Data-Conditioned Method Planning: Analyzes task data first to refine a coarse search space into executable, parallelizable "method routes."
- Verification-Guided Two-Stage Optimization: Broadly explores different routes first, then selectively exploits more promising candidates.
- Continuously checks validation protocols, metric calculations, and prediction artifacts throughout the optimization process to ensure auditability.
Tested on 20 medical imaging challenge tasks, AMID outperformed general MLE systems in the evaluation and approached or matched strong human baselines on certain tasks. Conclusion: such agentic workflows have the potential to transform medical imaging modeling from customized manual engineering into a more automated, auditable, high-performance pipeline.
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