Large Language Models Applied to Health Care Tasks May Improve Clinical Efficiency, Value of Care Rendered, Research, and Medical Education

Abstract

Large language models (LLMs) are generative artificial intelligence models that create content on the basis of the data on which it was trained. Processing capabilities have evolved from text only to being multimodal including text, images, audio, and video features. In health care settings, LLMs are being applied to several clinically important areas, including patient care and workflow efficiency, communications, hospital operations and data management, medical education, practice management, and health care research. Under the umbrella of patient care, several core use cases of LLMs include simplifying documentation tasks, enhancing patient communication (interactive language and written), conveying medical knowledge, and performing medical triage and diagnosis. However, LLMs warrant scrutiny when applied to health care tasks, as errors may have negative implications for health care outcomes, specifically in the context of perpetuating bias, ethical considerations, and cost-effectiveness. Customized LLMs developed for more narrow purposes may help overcome certain performance limitations, transparency challenges, and biases present in contemporary generalized LLMs by curating training data. Methods of customizing LLMs broadly fall under 4 categories: prompt engineering, retrieval augmented generation, fine-tuning, and agentic augmentation, with each approach conferring different information-retrieval properties for the LLM. LEVEL OF EVIDENCE: Level V, expert opinion.

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