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

TL;DR

This paper introduces MACRO, a self-evolving medical AI agent that autonomously discovers and synthesizes effective multi-step tool sequences from clinical imaging data, improving adaptability and accuracy.

Contribution

The paper presents a novel experience-driven approach enabling medical AI agents to autonomously discover and synthesize composite tools, enhancing flexibility and robustness in clinical image interpretation.

Findings

Improves multi-step orchestration accuracy across datasets

Enhances cross-domain generalization of medical imaging agents

Demonstrates effective self-improvement with minimal supervision

Abstract

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 composition to experience-driven tool discovery. From verified execution trajectories, the agent autonomously identifies recurring effective multi-step tool sequences, synthesizes them into reusable composite tools, and registers these as new high-level primitives that continuously expand its behavioral repertoire. A lightweight image-feature memory grounds tool selection in a visual-clinical context, while a GRPO-like training loop reinforces reliable invocation of discovered composites, enabling closed-loop self-improvement with minimal supervision. Extensive experiments across diverse medical imaging datasets and tasks demonstrate that autonomous composite tool discovery consistently improves multi-step orchestration accuracy and cross-domain generalization over strong baselines and recent state-of-the-art agentic methods, bridging the gap between brittle static tool use and adaptive, context-aware clinical AI assistance. Code will be available upon acceptance.