Synthetic Patient-Physician Conversations Simulated by Large Language Models: A Multi-Dimensional Evaluation.

ABSTRACT:

Background

Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance.

Objective

To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient-physician interactions in plastic surgery scenarios.

Methods

Each model generated tr

SUBMITTER: Haider SA 

PROVIDER: S-EPMC12298718 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature