The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.

Journal: Eye (London, England)
Published Date: Jun 13, 2025

Abstract

Thorough investigations of end-users' awareness, acceptance, and concerns about ophthalmic artificial intelligence (AI) are essential to ensure its successful implementation. We conducted a literature review on the acceptance of ophthalmic AI to provide an overall insight and qualitatively analysed the quality of eligible studies using a psychological model. We identified sixteen studies and evaluated these studies based on four primary factors (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions) and four regulating factors (i.e., gender, age, experiences, and voluntariness of use) of the psychological model. We found that most of the eligible studies only emphasized performance expectancy and effort expectancy, and in-depth discussions on the effects of social influence, facilitating conditions, and relevant regulating factors were relatively inadequate. The overall acceptance of ophthalmic AI among specific groups, such as patients with different eye diseases, experts in ophthalmology, professionals in other fields, and the general population, is high. Nevertheless, more well-designed qualitative studies with clear definitions of acceptance and using proper psychological models with larger sample sizes involving other representative and multidisciplinary stakeholders worldwide are still warranted. In addition, because of the multifarious concerns of AI, such as the economic burden, patient privacy, model safety, model trustworthiness, public awareness, and proper regulations over accountability issues, it is imperative to focus on evidence-based medicine, conduct high-quality randomized controlled trials, and promote patient education. Comprehensive clinician training, privacy-preserving technologies, and the issue of cost-effectiveness are also indispensable to address the above concerns and further propel the overall acceptance of ophthalmic AI.

Authors

  • An Ran Ran
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Chun Ho Lui
    Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Yih-Chung Tham
    Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore.
  • Ching-Yu Cheng
    Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore.
  • Chiu Yu Lam
    Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Wai Lam Cheung
    Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Siu Ting Chan
    Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Hok Ngai Ma
    Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Raphael Walter L C Chow
    Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China.
  • Dawei Yang
    Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital Fudan University, Shanghai, China.
  • Ziqi Tang
    Department of Pharmaceutical Chemistry, Department of Bioengineering and Therapeutic Sciences, Institute for Neurodegenerative Diseases, and Bakar Computational Health Sciences Institute, University of California, San Francisco, 675 Nelson Rising Ln Box 0518, San Francisco, CA, 94143, USA.
  • T Y Alvin Liu
    Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD.
  • Clement C Tham
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Hong Kong Eye Hospital, Hong Kong Special Administrative Region, China; Prince of Wales Hospital, Hong Kong Special Administrative Region, China.
  • Carol Y Cheung
    Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China. Electronic address: carolcheung@cuhk.edu.hk.

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