Generative artificial intelligence (GAI) usage guidelines for scholarly publishing: a cross-sectional study of medical journals.

Journal: BMC medicine
PMID: 39934830

Abstract

BACKGROUND: Generative artificial intelligence (GAI) has developed rapidly and been increasingly used in scholarly publishing, so it is urgent to examine guidelines for its usage. This cross-sectional study aims to examine the coverage and type of recommendations of GAI usage guidelines among medical journals and how these factors relate to journal characteristics.

Authors

  • Shuhui Yin
    Applied Linguistics & Technology, Department of English, Iowa State University, Ames, IA, USA.
  • Simu Huang
    Center for Data Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Peng Xue
    National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Zhuoran Xu
    1 Dalio Institute of Cardiovascular Imaging New York-Presbyterian Hospital New York NY.
  • Zi Lian
    Center for Health Equity & Urban Science Education, Teachers College, Columbia University, New York, NY, USA.
  • Chenfei Ye
    Peng Cheng Laboratory, Shenzhen, Guangdong, China.
  • Siyuan Ma
    Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States.
  • Mingxuan Liu
    Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.
  • Yuanjia Hu
    Institute of Chinese Medical Sciences, State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Macao 999078, China.
  • Peiyi Lu
    Department of Social Work and Social Administration, University of Hong Kong, Hong Kong SAR, China. peiyilu@hku.hk.
  • Chihua Li
    Institute of Chinese Medical Sciences, University of Macau, Zhuhai, Macao SAR, China. chihuali@umich.edu.

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