Google Introduces ResidencyRL: Training AI Medical Agents via RL in Simulated Clinics
SRSchmidgall · x · 2026-08-11
Google introduces ResidencyRL, a reinforcement learning (RL) method for training medical AI agents through simulated multi-turn clinical encounters, mirroring how human physicians build expertise during residency.
- Training Mechanism: Trained Gemini using over 50K simulated patient encounters. Conversations last up to 60 turns, featuring adversarial patients who hide symptoms or resist advice.
- Reward Function: Structured around diagnostic accuracy, management, communication, documentation, and safety.
- Evaluation: Blindly evaluated by board-certified clinicians across 97 scenarios, achieving an 87.6% overall preference. It hit a 90.7% win rate in information gathering, preferred or tied in 96.9% of management safety cases, and reduced missed red flags by 31%.
- Generalization: Although trained exclusively on primary care, it outperformed the base model in specialist oncology, multi-visit longitudinal care (+8.3% management reasoning), and benchmarks like AgentClinic.
Related event: Google Introduces ResidencyRL for AI Doctor Training(2 posts)→
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