Advancing healthcare with large language models: A scoping review of applications and future directions.

Abstract

The release of ChatGPT has spurred the widespread adoption of generative large language models (LLMs) in healthcare. This scoping review systematically examines their use in healthcare. A systematic search was conducted using PubMed, a comprehensive and representative database on biomedical and health science, to identify studies published between January 1, 2023, and July 30, 2024. Studies were included if they assessed the performance of generative LLMs in healthcare applications; review or perspective articles were excluded. A total of 415 studies were included, with a significant increase in publications observed after April 2023. Generative LLMs were applied across various medical specialties, primarily supporting clinical decision-making (26.7%) and providing patient information (23.9%). Smaller proportions were focused on professional education and training (18.1%), research (16.1%), and workflow support (12.5%). These applications were mainly supported by three key NLP tasks: question answering (36.1%), text classification (27.5%), and text generation (26.3%). Public datasets appeared in 20% of studies, and 15% used clinical patient data. Of the 98 LLMs used, GPT-4 (51.3%), GPT-3.5 (36.6%), and ChatGPT (22.4%) were the most common. Direct prompting was the most common adaptation method (92.5%), with reinforcement learning rarely utilized (1.4%). Accuracy was the most frequently assessed metric, while errors and safety (9.4%) and time efficiency (7.0%) were less commonly evaluated. LLMs hold promise across healthcare applications. Expanding their use in workflow optimization, trainee education, and research tools could enhance healthcare delivery and innovation. Comprehensive evaluation using standardized criteria is essential for LLMs integration into healthcare.