WV-MOS

SIMAX framework generates annotated synthetic clinician-patient dialogues for AI communication coding evaluation

Researchers introduce SIMAX, a framework for generating controlled, annotated synthetic clinician-patient dialogues to support development and evaluation of AI-driven clinical communication coding systems. The framework produces dialogues with reference behavioral annotations using two codebooks (Global and WISER), generating 3,388 simulated dialogues across three medical specialties with varied personas and accent conditions. Evaluation shows reasonable speech naturalness and high transcription fidelity, with downstream testing revealing the framework can expose sensitivity gaps in communication coding systems. The work addresses a data scarcity bottleneck in deploying ambient AI scribes in clinical settings.