arXiv:2607.01470v1 Announce Type: new Abstract: Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for RL from world feedback: once clinical SMEs encode decision logic into a verifier, that verifier grades unlimited rollouts without per-episode annotation. But applying RL requires a sound feedback channel and sufficient base capability. We audit MedAgentBench v1/v2, find a 41.7\% silent-finish ceiling that makes inaction the RL dominant strategy, and construct \textbf{MedAgentBench-v3 (MAB-v3)} (5
The proliferation of AI in healthcare demands robust evaluation frameworks, and the current limitations of existing benchmarks are becoming critical as clinical AI agents advance.
This work directly addresses a key challenge in developing reliable clinical AI agents, specifically the feedback mechanisms required for reinforcement learning in complex medical environments.
The introduction of MedAgentBench-v3 provides a more accurate and effective benchmark for the development and testing of clinical AI agents, overcoming previous limitations that incentivized inaction.
- · AI developers in healthcare
- · Patients receiving AI-driven care
- · Healthcare technology companies
- · Researchers in reinforcement learning
- · Developers relying on flawed benchmarks
- · Clinical AI agents developed with poor feedback loops
Improved training and evaluation of autonomous AI agents in clinical settings.
Accelerated development and adoption of AI-powered diagnostic and treatment planning tools in healthcare.
Enhanced patient outcomes and efficiency in medical practice through more reliable AI integration.
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Read at arXiv cs.AI