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Diagnosis (Berlin, Germany)

Early evidence for a multi-agent AI simulator for clinical reasoning practice: performance, consistency, and challenges

In Fall 2024, 175 second-year medical students completed three MAESSCR multi-agent LLM clinical encounters as coursework, and six clinician-educators rated 120 randomly sampled transcripts with a dichotomous tool, finding 92% (110/120) adherence to scripted details, 2.5% (3/120) diagnosis-changing information, 12% (14/120) unrealistic patient portrayal, and 17% (20/120) technical issues, while the most prominent problem was agents interpreting findings before students could, occurring in 28% (34/120) of encounters for history/physical exam agents and 59% (71/120) for diagnostics/management agents, with most disruptions judged minor.