ResidencyRL trains clinical agents in simulated consultations
Research explores reinforcement learning in simulated clinical consultations, involving patient history, diagnostic hypotheses, and decisions under uncertainty.
The article describes training clinical agents with reinforcement learning in simulated environments that represent consultations and provide feedback. The approach considers stages of clinical reasoning: gathering a history, refining diagnostic hypotheses, and making decisions under uncertainty.
The work aims to develop this reasoning beyond static medical benchmarks. If you use AI to study or apply the research, do not enter medical records, names, or other identifiable data without authorization; use synthetic or anonymized examples and follow your organization’s rules.