MedEvoEval: Evaluating Continual Evolution of Doctor Agents through Simulated Clinical Episodes
arXiv:2606.28900v1 Announce Type: new Abstract: Doctor agents are moving beyond single-turn answer generation toward evolving clinical decision systems. Within an outpatient episode, they acquire evidence, use examination and consultation resources, and decide when to finalize a diagnosis and management plan. Across episodes, their behavior may change through memory, retrieval, reflection, or other update mechanisms. Current evaluations only partially cover this setting. Fixed-input medical QA benchmarks score final answers from complete inputs, whereas many interactive benchmarks still focus
The rapid advancement in AI capabilities for complex reasoning and agentic behavior makes the development of 'doctor agents' a logical next step in applying AI to healthcare.
This development indicates a move towards more autonomous and capable medical AI, potentially transforming clinical decision-making, healthcare delivery, and the role of human practitioners.
AI systems are evolving from static question-answering tools to dynamic, interactive agents that can adapt and improve over time in complex clinical scenarios.
- · AI development firms
- · Healthcare providers
- · Patients
- · Traditional medical software companies
- · Healthcare professionals resistant to AI integration
Doctor agents will begin to augment, and in some cases replace, certain diagnostic and management tasks currently performed by human clinicians.
The proliferation of highly capable medical AI agents will necessitate new regulatory frameworks for AI accountability, liability, and patient safety.
The integration of evolving AI agents could lead to a restructuring of medical education, focusing on AI oversight and complex, uniquely human aspects of care.
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Read at arXiv cs.AI