Large language models and synthetic health data: progress and prospects

Abstract

Abstract Objectives Given substantial obstacles surrounding health data acquisition, high-quality synthetic health data are needed to meet a growing demand for the application of advanced analytics for clinical discovery, prediction, and operational excellence. We highlight how recent advances in large language models (LLMs) present new opportunities for progress, as well as new risks, in synthetic health data generation (SHDG). Materials and Methods We synthesized systematic scoping reviews in the SHDG domain, recent LLM methods for SHDG, and papers investigating the capabilities and limits of LLMs. Results We summarize the current landscape of generative machine learning models (eg, Generative Adversarial Networks) for SHDG, describe remaining challenges and limitations, and identify how recent LLM approaches can potentially help mitigate them. Discussion Six research directions are outlined for further investigation of LLMs for SHDG: evaluation metrics, LLM adoption, data efficiency, generalization, health equity, and regulatory challenges. Conclusion LLMs have already demonstrated both high potential and risks in the health domain, and it is important to study their advantages and disadvantages for SHDG.

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