Synthetic Patient Physician Dialogue

Synthetic patient-physician dialogue generation leverages large language models (LLMs) to create realistic conversational data for training and evaluating medical dialogue systems, addressing the scarcity of real-world, privacy-protected data. Current research focuses on improving the quality and realism of these synthetic dialogues, exploring techniques like iterative refinement with feedback loops, and employing multi-agent or mixture-of-experts LLM architectures for enhanced performance and bilingual capabilities. This work is significant because it facilitates the development of more effective and accessible medical communication tools, potentially improving patient care and reducing the burden on healthcare professionals.