Diagnosis Dialogue

Diagnosis dialogue research focuses on developing AI systems capable of conducting clinically relevant conversations to aid in medical diagnosis, mirroring the crucial physician-patient interaction. Current efforts leverage large language models (LLMs) often enhanced with external planners or neuro-symbolic frameworks to improve question generation, manage the diagnostic process, and ensure safety and explainability, particularly in mental health applications. These systems are evaluated using novel metrics that assess diagnostic accuracy, communication skills, and adherence to clinical guidelines, with a growing emphasis on creating and utilizing high-quality datasets of simulated or anonymized patient conversations. This work aims to improve diagnostic efficiency, accessibility, and consistency, potentially transforming healthcare delivery.