This paper introduces ResidencyRL, a novel reinforcement learning framework designed to train clinical AI agents through extensive simulated multi-turn patient encounters. The method significantly improves diagnostic accuracy, management quality, and patient-centered communication, demonstrating robust and generalizable capabilities for sequential clinical decision-making.

This article proposes a practical framework for engineering measurable trust in clinical AI systems, moving beyond subjective impressions of model performance. It emphasizes integrating evidence, human supervision, and staged autonomy within a multi-layered architecture to ensure safety and accountability in healthcare applications.