Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine.

Journal: Journal of medical Internet research
Published Date:

Abstract

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.

Authors

  • Kuo Zhang
    Wuhan EndoAngel Medical Technology Company, Wuhan, China.
  • Xiangbin Meng
    Pengcheng Laboratory, Shenzhen, Guangdong, China.
  • Xiangyu Yan
    School of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
  • Jiaming Ji
  • Jingqian Liu
    China Telecom, Beijing, China.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Heng Zhang
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Da Liu
    Heilongjiang Provincial Key Laboratory of Oilfield Applied Chemistry and Technology, School of Chemical Engineering, Daqing Normal University, Daqing 163712, China.
  • Jingjia Wang
    Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.
  • Xuliang Wang
    Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.
  • Jun Gao
    Physics of Complex Fluids, MESA+ Institute for Nanotechnology, University of Twente, Enschede 7500 AE, The Netherlands.
  • Yuan-Geng-Shuo Wang
    Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.
  • Chunli Shao
    Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.
  • Wenyao Wang
    Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.
  • Jiarong Li
    College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, People's Republic of China.
  • Ming-Qi Zheng
    Department of Cardiology, the First Hospital of Hebei Medical University, Graduate School of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Yaodong Yang
    School of Mechanical Engineering (Shandong Institute of Mechanical Design and Research), Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China.
  • Yi-Da Tang
    Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.