The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
Scaling medical agents from assistance to autonomy.Abstract
The growing ability of large language models and vision-language models to jointly interpret and reason over images and text is reshaping medical agents, moving them from task-specific predictors toward autonomous systems that perceive, reason, plan, remember, and act in clinical environments. This work departs from the capability-first perspective of existing literature and instead begins from clinical deployment, asking what tasks, contamination-resistant benchmarks, and interactive training environments are required before medical agents can be trusted in practice. Medical agents are formalized as sequential decision-making systems under partial observability, together with a three-level autonomy taxonomy spanning assisted, cooperative, and fully autonomous operation. The field is organized along a unified scaling spine consisting of framework scaling, capability scaling, and environment scaling. Within this framework, clinical environment scaling, the integration of tools, data, and clinical gyms, is identified as the most actionable yet underexplored direction for agents operating in PACS, EHR, and FHIR ecosystems. Clinical self-evolution, where agents improve through interaction with their environments rather than parameter scaling alone, is further positioned as a key research frontier. By consolidating more than 300 references, with particular emphasis on advances from 2025 to 2026, this work provides a roadmap toward trustworthy, self-improving medical imaging systems for real clinical practice.
This survey reframes medical agents from the perspective of clinical deployment. It introduces a three-level autonomy taxonomy, organizes the field around framework, capability, and environment scaling, and highlights clinical self-evolution as a key frontier for trustworthy medical AI systems in real-world clinical practice.
