Large Language Models Illuminate a Progressive Pathway to Artificial
  Healthcare Assistant: A Review
Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Yang Chen Li Zhang Lin Shen Bin Dong
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 paper provides a comprehensive review on the applications and
implications of LLMs in medicine. It begins by examining 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 then focuses on multimodal LLMs, investigating their
ability to process diverse data types like medical imaging and EHRs to augment
diagnostic accuracy. To address LLMs' limitations regarding personalization and
complex clinical reasoning, the paper explores the emerging development of
LLM-powered autonomous agents for healthcare. Furthermore, it summarizes the
evaluation methodologies for assessing LLMs' reliability and safety in medical
contexts. Overall, this review offers an extensive analysis on the
transformative potential of LLMs in modern medicine. It also highlights the
pivotal need for continuous optimizations and ethical oversight before these
models can be effectively integrated into clinical practice. Visit
https://github.com/mingze-yuan/Awesome-LLM-Healthcare for an accompanying
GitHub repository containing latest papers.