Zhang Kuo, Meng Xiangbin, Yan Xiangyu, Ji Jiaming, Liu Jingqian, Xu Hua, Zhang Heng, Liu Da, Wang Jingjia, Wang Xuliang, Gao Jun, Wang Yuan-Geng-Shuo, Shao Chunli, Wang Wenyao, Li Jiarong, Zheng Ming-Qi, Yang Yaodong, Tang Yi-Da
Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Pengcheng Laboratory, Shenzhen, Guangdong, China.
J Med Internet Res. 2025 Jan 7;27:e59069. doi: 10.2196/59069.
Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs' evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.
大型语言模型（LLMs）正在迅速推动医学人工智能发展，给医疗保健带来变革性变化。这些模型在自然语言处理（NLP）方面表现出色，可增强临床支持、诊断、治疗和医学研究。诸如GPT-4和BERT（来自Transformer的双向编码器表示）等突破，通过提升计算能力和数据展示了大型语言模型的演进。然而，它们对硬件的高要求正通过技术进步得到解决。大型语言模型在处理多模态数据方面独具特色，从而改善急诊、老年护理和数字医疗程序。挑战包括确保其经验可靠性，解决伦理和社会影响，尤其是数据隐私问题，并在维护隐私和问责制的同时减轻偏差。本文强调需要以人类为中心、无偏差的大型语言模型用于个性化医疗，并倡导公平发展和获取。大型语言模型有望在医疗保健领域产生变革性影响。
