Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery

ArXi:2603.05860v1 Announce Type: new 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.