Large Language Models in Healthcare: A Review

Abstract

Large Language Models (LLMs) have revolutionized the field of natural language processing through their capabilities to understand and generate human language. Their application now extends to various fields, including healthcare, which has led to numerous initiatives aiming to adapt general-purpose LLMs to the medical field. This review surveys existing LLMs designed for medical applications and describes their adaptation methods, training data, and fine-tuning techniques. More specifically, this review traces the evolution of medical LLMs starting from traditional pre-trained language models to the current state of LLMs. Finally, we summarize the major challenges and limitations faced by LLMs in the healthcare contexts, including data scarcity, domain-specific bias, ethical concerns, and the limited interpretability of model outputs in clinical decision-making.

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