SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models
This addresses a gap in privacy-compliant synthetic data for medical AI research, with applications in education and decision support, though it is incremental as it builds on existing LLM-based synthetic data generation methods.
The paper tackled the problem of generating synthetic physician-to-physician discussions for medical AI, using a framework that combines structured prompting with de-identified metadata, achieving high communication effectiveness (mean 4.4/5) and medical content quality (mean 4.1/5) with 91% clinical relevance.
Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and even participate in subsequent interactions. However, privacy regulations and ethical considerations severely restrict access to such data. While synthetic data generation using Large Language Models offers a promising alternative, existing approaches primarily focus on patient-physician interactions or structured medical records, leaving a significant gap in physician-to-physician communication synthesis. We present SynDocDis, a novel framework that combines structured prompting techniques with privacy-preserving de-identified case metadata to generate clinically accurate physician-to-physician dialogues. Evaluation by five practicing physicians in nine oncology and hepatology scenarios demonstrated exceptional communication effectiveness (mean 4.4/5) and strong medical content quality (mean 4.1/5), with substantial interrater reliability (kappa = 0.70, 95% CI: 0.67-0.73). The framework achieved 91% clinical relevance ratings while maintaining doctors' and patients' privacy. These results place SynDocDis as a promising framework for advancing medical AI research ethically and responsibly through privacy-compliant synthetic physician dialogue generation with direct applications in medical education and clinical decision support.