Generative Large Language Models for Healthcare Applications: Opportunities, Challenges, and Future Directions

Abstract

Generative Large Language Models have recently become a powerful avenue for natural language processing similar to what humans would write, given previously written text. They have demonstrated impressive performance in generating human-like text, spanning diverse areas and domains. In healthcare, where vast amounts of text data are generated daily, GLLMs hold great potential in clinical documentation, patient interactions, medical discovery, and clinical decision-making systems. This paper comprehensively explores potential applications of large language models in healthcare. Specifically, it assesses the opportunities, challenges, and exciting new opportunities. We also examine the state-of-the-art in GLLMs and how they are adapted and fine-tuned for use in healthcare. Additionally, we consider ethical, privacy, and regulatory issues and potential implications for clinical practice, education, clinical practice, and biomedical research. The overall goal is to demystify potential applications of GLLMs in advancing healthcare, improving patient outcomes, and facilitating medical innovation.