Patients' Preferences for Artificial Intelligence Applications Versus Clinicians in Disease Diagnosis During the SARS-CoV-2 Pandemic in China: Discrete Choice Experiment.

Journal: Journal of medical Internet research
PMID: 33493130

Abstract

BACKGROUND: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19.

Authors

  • Taoran Liu
    Faculty of Economics and Business, University of Groningen, Groningen, Netherlands.
  • Winghei Tsang
    Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
  • Fengqiu Huang
    Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
  • Oi Ying Lau
    Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
  • Yanhui Chen
    Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China.
  • Jie Sheng
    School of Architecture and Urban Planning, Kunming University of Science and Technology, Kunming, China.
  • Yiwei Guo
    School of Finance and Business, Shanghai Normal University, Shanghai, China.
  • Babatunde Akinwunmi
    Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, MA, United States.
  • Casper Jp Zhang
    School of Public Health, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Wai-Kit Ming
    International School, Jinan University, Guangzhou, China.

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