Large Language Models in Healthcare: A Review

Abstract

This paper examines the potential of large language models (LLMs) in the healthcare sector, delving into their prospective applications, challenges, and future trajectories. LLMs have demonstrated encouraging results in various healthcare-related domains, including the development of clinical decision support systems, natural language processing in electronic health records, healthcare question/answer systems, and healthcare education. However, integrating these models into healthcare practice also raises several concerns, such as data privacy and security issues, the requirement for vast amounts of training data, model biases, and the limited interpretability of model predictions. Overcoming these hurdles necessitates a collaborative effort from experts across multiple disciplines. Despite these obstacles, the deployment of LLMs in healthcare holds the potential to transform the industry and significantly enhance patient outcomes.

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