Assessing the influence of generative artificial intelligence (GenAI) on awareness and behavior in medical research integrity: An online survey study.

Journal: Accountability in research
Published Date: Sep 5, 2025

Abstract

BACKGROUND: Generative Artificial Intelligence(GenAI) significantly enhances medical research efficiency but raises ethical concerns regarding research integrity. The lack of systematic guidelines for its ethical use underscores the need to investigate GenAI's impact on researchers' awareness and behavior concerning integrity. METHODS/MATERIALS: A cross-sectional survey of 718 valid responses from Chinese medical researchers assessed GenAI's impact on research integrity using an extended Unified Theory of Acceptance and Use of Technology(UTAUT) model. RESULTS: The findings reveal that performance expectancy, effort expectancy, technical environment, trust in technology, and supporting conditions positively influence researchers' awareness of research integrity. Conversely, GenAI anxiety and perceived risks exert a significant negative impact. Furthermore, both supporting conditions and integrity awareness are positively associated with integrity behavior, while GenAI anxiety negatively affects such behavior. CONCLUSION: The stakeholders in the medical research ecosystem should develop comprehensive guidelines for the responsible use of GenAI. Emphasis should be placed on optimizing the technical environment, enhancing trust and support structures, and embedding integrity safeguards, thereby promoting the synergistic development of technological innovation and ethical research practices.

Authors

  • Xiaoting Peng
    Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080, Guangzhou, Guangdong Province, China.
  • Yufeng Cai
    School of Life Sciences, Central South University, Changsha, China.
  • Dehua Hu
    Institute of Information Security and Big Data, Central South University, Changsha 410083, Hunan, China.
  • Yi Guo
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
  • Haixia Liu
    Central South University, Changsha, Hunan, CN.
  • Xusheng Wu
    The First Clinical Medical College of Gansu University of Chinese Medicine, Gansu Provincial Hospital, Lanzhou, China.
  • Qingyuan Hu
    Department of Scientific Research, The Third Xiangya Hospital, Central South University, Changsha, China.

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