Yu Ping, Xu Hua, Hu Xia, Deng Chao
School of Computing and Information Technology, University of Wollongong, Wollongong, NSW 2522, Australia.
Section of Biomedical Informatics and Data Science, Yale School of Medicine, 100 College Street, Fl 9, New Haven, CT 06510, USA.
Healthcare (Basel). 2023 Oct 20;11(20):2776. doi: 10.3390/healthcare11202776.
Generative artificial intelligence (AI) and large language models (LLMs), exemplified by ChatGPT, are promising for revolutionizing data and information management in healthcare and medicine. However, there is scant literature guiding their integration for non-AI professionals. This study conducts a scoping literature review to address the critical need for guidance on integrating generative AI and LLMs into healthcare and medical practices. It elucidates the distinct mechanisms underpinning these technologies, such as Reinforcement Learning from Human Feedback (RLFH), including few-shot learning and chain-of-thought reasoning, which differentiates them from traditional, rule-based AI systems. It requires an inclusive, collaborative co-design process that engages all pertinent stakeholders, including clinicians and consumers, to achieve these benefits. Although global research is examining both opportunities and challenges, including ethical and legal dimensions, LLMs offer promising advancements in healthcare by enhancing data management, information retrieval, and decision-making processes. Continued innovation in data acquisition, model fine-tuning, prompt strategy development, evaluation, and system implementation is imperative for realizing the full potential of these technologies. Organizations should proactively engage with these technologies to improve healthcare quality, safety, and efficiency, adhering to ethical and legal guidelines for responsible application.
以ChatGPT为代表的生成式人工智能（AI）和大语言模型（LLM）有望彻底改变医疗保健和医学领域的数据与信息管理。然而，针对非人工智能专业人员指导其整合应用的文献却很少。本研究开展了一项范围综述，以满足将生成式人工智能和大语言模型整合到医疗保健和医学实践中对指导的迫切需求。它阐明了支撑这些技术的独特机制，如基于人类反馈的强化学习（RLFH），包括少样本学习和思维链推理，这使其有别于传统的基于规则的人工智能系统。要实现这些益处，需要一个包容性的、协作性的共同设计过程，让所有相关利益者参与进来，包括临床医生和消费者。尽管全球研究正在审视包括伦理和法律层面在内的机遇与挑战，但大语言模型通过加强数据管理、信息检索和决策过程，在医疗保健领域提供了有前景的进展。为了充分发挥这些技术的潜力，在数据采集、模型微调、提示策略开发、评估和系统实施方面持续创新势在必行。各组织应积极采用这些技术，以提高医疗保健质量、安全性和效率，并遵守负责任应用的伦理和法律准则。
