Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

Abstract

With the rapid development of artificial intelligence, large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This has sparked significant interest in applying LLMs to enhance various aspects of healthcare, ranging from medical education to clinical decision support. However, medicine involves multifaceted data modalities and nuanced reasoning skills, presenting challenges for integrating LLMs. This review introduces the fundamental applications of general-purpose and specialized LLMs, demonstrating their utilities in knowledge retrieval, research support, clinical workflow automation, and diagnostic assistance. Recognizing the inherent multimodality of medicine, the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic accuracy. To address LLMs’ limitations regarding personalization and complex clinical reasoning, the review further explores the emerging development of LLM-powered autonomous agents for healthcare. Moreover, it summarizes the evaluation methodologies for assessing LLMs’ reliability and safety in medical contexts. LLMs have transformative potential in medicine; however, there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

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