Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery
Clinical image interpretation is inherently multi-step and tool-centric: clinicians iteratively combine visual evidence with patient context, quantify findings, and refine their decisions through a sequence of specialized procedures. While LLM-based agents promise to orchestrate such heterogeneous medical tools, existing systems treat tool sets and invocation strategies as static after deployment. This design is brittle under real-world domain shifts, across tasks, and evolving diagnostic requirements, where predefined tool chains frequently degrade and demand costly manual re-design. We propose MACRO, a self-evolving, experience-augmented medical agent that shifts from static tool compos...
| Authors | Lin Fan, Pengyu Dai, Zhipeng Deng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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