PatientLM: Controllable Patient Simulation for Interactive Medical Evaluation and Training

Medical assistants are increasingly used in multi-turn consultations, where patients progressively disclose information and adapt their follow-up to assistant responses. However, existing multi-turn evaluation protocols typically rely on fixed dialogue contexts or predefined patient-side trajectories. Such protocols cannot measure how an assistant elicits information or shapes subsequent patient behavior. We introduce **PatientLM**, a patient simulator that combines a trained language model with a controller for specific medical interaction scenarios. PatientLM represents each scenario with a structured patient intent that specifies the patient role and goal, together with the clinical information and requests to disclose over the course of the dialogue. A controller governs this progression, while the trained language model expresses the currently available information naturally in response to the evolving interaction. Simulator evaluations show that PatientLM most completely communicates case-specified information and requests and achieves the strongest overall performance on naturalness and other simulator-quality measures. We then use PatientLM to unify different multi-turn dialogue evaluation paradigms under a common interactive protocol, and experiments show that assistant performance is generally lower than in the original evaluations, suggesting that fixed patient trajectories may overestimate interactive performance. PatientLM also supports effective online SFT for smaller assistant models, improving their factuality and reliability. These results highlight controllable patient simulation as a promising foundation for advancing interactive medical AI.