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Volume 15 Issue 3
May  2024
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LU Yao, LIU Jianing, WANG Mian, HUANG Jiajie, HAN Baojin, SUN Mingyao, CHENG Qianji, NING Jinling, GE Long. Artificial Intelligence in Shared Decision Making[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(3): 661-667. DOI: 10.12290/xhyxzz.2023-0209
Citation: LU Yao, LIU Jianing, WANG Mian, HUANG Jiajie, HAN Baojin, SUN Mingyao, CHENG Qianji, NING Jinling, GE Long. Artificial Intelligence in Shared Decision Making[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(3): 661-667. DOI: 10.12290/xhyxzz.2023-0209
shu

Artificial Intelligence in Shared Decision Making

  • 1.

    Department of Health Policy and Management, School of Public Health, Lanzhou University, Lanzhou 730000, China

  • 2.

    Evidence-based Social Science Research Center, School of Public Health, Lanzhou University, Lanzhou 730000, China

  • 3.

    College of Nursing, Gansu University of Chinese Medicine, Lanzhou 730030, China

  • 4.

    Department of Oncology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China

  • 5.

    Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China

  • 6.

    Evidence-based Nursing Center, School of Nursing, Lanzhou University, Lanzhou 730000, China

  • 7.

    Key Laboratory of Evidence-based Medicine and Knowledge Translation of Gansu Province, Lanzhou 730000, China

Funds: 

China Academy of Chinese Medical Sciences Innovation Fund CI2021A05502

More Information
  • Corresponding author:

    GE Long, E-mail: gelong2009@163.com

  • Received Date: April 22, 2023
  • Accepted Date: August 08, 2023
  • Available Online: August 22, 2023
  • Publish Date: August 21, 2023
  • Issue Publish Date: May 29, 2024
    Graphical Abstract
    • Abstract

  • Artificial intelligence(AI) empowers the development of the medical industry, providing precise and intelligent assistance for clinical diagnosis, treatment, and rehabilitation.AI has the potential to facilitate shared decision making (SDM), but AI interventions used for SDM are currently in their infancy, presenting both challenges and opportunities. This paper aims to describe the application of AI in SDM, explore the problems and challenges of AI-based decision aid used for SDM, and propose possible solutions, aiming to provide a guide for the development and implementation of AI-based decision aid.

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    • References (64)
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