Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data.

ABSTRACT:

Background

Clinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.

Objective

This study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI's GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source

SUBMITTER: Barr AA 

PROVIDER: S-EPMC11836953 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature