Simulation training improves language models for psychiatric interviews
Training language models in simulation for psychiatric intake
Artificial IntelligenceComputation and Language
Summary
Psychiatric intake interviews are important and complex conversations between doctors and patients to understand mental health. The authors trained a language model by having it practice simulated interviews using reinforcement learning, which helped it get better at asking questions, making diagnoses, and planning care. When tested in role-played scenarios with real psychiatrists, the trained model performed better than the original. The researchers also created tests to check if the model safely recognizes urgent patient risks and found the training reduced missed dangers.
What this means in practice
- •For mental health technology developers: Train conversational agents for psychiatric interviews to improve diagnosis accuracy and patient communication safely.$Commercial implications: Enables development of AI tools that assist clinicians with structured psychiatric interviews, improving care delivery and workflow.
- •For clinical training teams: Use simulation-trained language models as standardized patient role-players to help train psychiatrists in intake interviewing skills.
Authors
Samuel Schmidgall, Tim Strother, Alex Bijamov, Min Woo Sun, Anil Palepu, Akshay Goel, Ulysse Marteau-Ferey, Théo Moutakanni, Yubin Kim, Joseph Breda, Christina Chen, Justin Chen, Paul Jhun, Andrew Marshall, Sarah Montgomery-Taylor, Eun Kyoung Hong, Shashank Ravi, Kameron C. Black, David J. H. Wu, Austin Schoeffler, John Lyon Havlik, Elizabeth C. Stade, Jared Moore, Johannes C. Eichstaedt, Logan Pierce, Jeff Jopling, Johannes Thrul, Thomas H. McCoy, Neguine Rezaii, Mark S. Chen, Ellen E. Fitzsimmons-Craft, Shirley Wang, Desmond C. Ong, Katharina Schultebraucks, Torrey Creed, Bhavna Daryani, Aleksandra Faust, S. Sara Mahdavi, Caroline Figueroa, Eric Lin, Kate H. Bentley, Ernest Lim, Zhihan Jiang, Xuhai "Orson" Xu, Daniel McDuff, John Torous, Yun Liu, Po-Hsuan Cameron Chen, Mike Schaekermann, Joelle Barral, Alvin Rajkomar, Tao Tu, Lin Yang, Isaac Galatzer-Levy, David Racz, Valentin Liévin
Abstract
In psychiatric practice, the clinical intake interview remains a primary diagnostic instrument, requiring the clinician to establish rapport while disentangling complex, overlapping presentations. Here, we show that multi-turn reinforcement learning (RL) in simulation environments improves language model performance on structured text-based psychiatric intake, spanning history-taking, differential diagnosis, and initial management planning. In a randomized, double-blind crossover study ($N = 60$ matched scenario pairs, 120 encounters) comparing the RL-trained agent against its identically prompted base model, 32 board-certified psychiatrists role-played standardized patients while a separate panel of 15 independent, board-certified psychiatrists scored the de-identified transcripts. The trained agent outperformed the base model in 76.7% of matched scenario pairs ($p < 0.0001$), with gains on an adapted clinical-examination rubric ($d_z = 0.77$, $p < 0.0001$), patient-centered communication (82.0% vs. 65.2%, $p < 0.001$), and differential diagnosis appropriateness (68.3% vs. 33.3%, $p < 0.001$). Across 127 psychiatrist-validated simulated intake scenarios, the trained agent nearly doubled its base model's clinical rubric score (0.706 vs. 0.379; composite reward 0.564 vs. 0.294, $p < 0.0001$). To systematically probe conversational safety, we established two Delphi-consensus taxonomies, of clinical safety risks and relational competencies, from which 256 challenging adversarial presentations were derived; we find that training in simulation environments more than halved under-triage of acute safety risks (12.9% vs. 30.5%, $p < 0.0001$). Simulation environments thus provide a controlled testbed for training and stress-testing psychiatric conversational agents, with gains that transferred to psychiatrist-enacted encounters, though residual gaps require safeguards and clinician oversight.