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

Eye volume 39, pages 2353–2362 (2025)Cite this article

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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.

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All data used for the review have been included in the manuscript.

References

  1. Nakayama LF, Ribeiro LZ, Malerbi FK, Regatieri CVS. Ophthalmology and artificial intelligence: present or future? A diabetic retinopathy screening perspective of the pursuit for fairness. Front Ophthalmol. 2022;2:898181.

  2. Wu X, Chen J, Yun D, Yuan M, Liu Z, Yan P, et al. Effectiveness of an ophthalmic hospital-based virtual service during the COVID-19 pandemic. Ophthalmology. 2021;128:942–5.

    Article  PubMed  Google Scholar 

  3. Han JED, Liu X, Bunce C, Douiri A, Vale L, Blandford A, et al. Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease (HERMES): a cluster randomised superiority trial with a linked Diagnostic Accuracy Study-HERMES study report 1-study protocol. BMJ Open. 2022;12:e055845.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hallak JA, Scanzera AC, Azar DT, Chan RVP. Artificial intelligence in ophthalmology during COVID-19 and in the post COVID-19 era. Curr Opin Ophthalmol. 2020;31:447–53.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kelly S, Kaye S-A, Oviedo-Trespalacios O. What factors contribute to the acceptance of artificial intelligence? A systematic review. Telemat Inform. 2023;77:101925.

  6. Lai MC, Brian M, Mamzer MF. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med. 2020;18:14.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Esmaeilzadeh P, Mirzaei T, Dharanikota S. Patients’ perceptions toward human-artificial intelligence interaction in health care: experimental study. J Med Internet Res. 2021;23:e25856.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Front Artif Intell. 2020;3:578983.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shinners L, Aggar C, Grace S, Smith S. Exploring healthcare professionals’ perceptions of artificial intelligence: validating a questionnaire using the e-Delphi method. Digit Health. 2021;7:20552076211003433.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Shinners L, Grace S, Smith S, Stephens A, Aggar C. Exploring healthcare professionals’ perceptions of artificial intelligence: piloting the Shinners Artificial Intelligence Perception tool. Digit Health. 2022;8:20552076221078110.

    PubMed  PubMed Central  Google Scholar 

  11. Wu C, Xu H, Bai D, Chen X, Gao J, Jiang X. Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis. BMJ Open. 2023;13:e066322.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Yakar D, Ongena YP, Kwee TC, Haan M. Do people favor artificial intelligence over physicians? A survey among the general population and their view on artificial intelligence in medicine. Value Health. 2022;25:374–81.

    Article  PubMed  Google Scholar 

  13. Tseng R, Gunasekeran DV, Tan SSH, Rim TH, Lum E, Tan GSW, et al. Considerations for artificial intelligence real-world implementation in ophthalmology: providers’ and patients’ perspectives. Asia Pac J Ophthalmol. 2021;10:299–306.

    Article  Google Scholar 

  14. Ayaz A, YanartaĹź M. An analysis on the unified theory of acceptance and use of technology theory (UTAUT): acceptance of electronic document management system (EDMS). Comput Hum Behav Rep. 2020;2:100032.

  15. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. Mis Q. 2003;27:425–78.

    Article  Google Scholar 

  16. Lambert SI, Madi M, Sopka S, Lenes A, Stange H, Buszello CP, et al. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digital Med. 2023;6:111.

    Article  Google Scholar 

  17. Keel S, Lee PY, Scheetz J, Li ZX, Kotowicz MA, MacIsaac RJ, et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci Rep. 2018;8:4330.

  18. Lin H, Li R, Liu Z, Chen J, Yang Y, Chen H, et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine. 2019;9:52–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Scheetz J, Koca D, McGuinness M, Holloway E, Tan Z, Zhu Z, et al. Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia. Sci Rep. 2021;11:15808.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shah P, Mishra D, Shanmugam M, Vighnesh MJ, Jayaraj H. Acceptability of artificial intelligence-based retina screening in general population. Indian J Ophthalmol. 2022;70:1140–4.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Yap A, Wilkinson B, Chen E, Han L, Vaghefi E, Galloway C, et al. Patients perceptions of artificial intelligence in diabetic eye screening. Asia Pac J Ophthalmol. 2022;11:287–93.

    Article  Google Scholar 

  22. Whitestone N, Nkurikiye J, Patnaik JL, Jaccard N, Lanouette G, Cherwek DH, et al. Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. Br J Ophthalmol. 2024;108:840–5.

    Article  PubMed  Google Scholar 

  23. Zheng B, Wu MN, Zhu SJ, Zhou HX, Hao XL, Fei FQ, et al. Attitudes of medical workers in China toward artificial intelligence in ophthalmology: a comparative survey. BMC Health Serv Res. 2021;21:1067.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Valikodath NG, Al-Khaled T, Cole E, Ting DSW, Tu EY, Campbell JP, et al. Evaluation of pediatric ophthalmologists’ perspectives of artificial intelligence in ophthalmology. J AAPOS. 2021;25:164.e1–e5.

    Article  PubMed  Google Scholar 

  25. Gunasekeran DV, Zheng F, Lim GYS, Chong CCY, Zhang S, Ng WY, et al. Acceptance and perception of artificial intelligence usability in eye care (APPRAISE) for ophthalmologists: a multinational perspective. Front Med. 2022;9:875242.

    Article  Google Scholar 

  26. Ho S, Doig GS, Ly A. Attitudes of optometrists towards artificial intelligence for the diagnosis of retinal disease: a cross-sectional mail-out survey. Ophthalmic Physiol Opt. 2022;42:1170–9.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Scanzera AC, Shorter E, Kinnaird C, Valikodath N, Al-Khaled T, Cole E, et al. Optometrist’s perspectives of artificial intelligence in eye care. J Optom. 2022;15:S91–7.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Alwadani F, Zakaria O, Alwadany M, Alwadani A, Alarfaj M, Mustafa W, et al. Ophthalmologists’ view of artificial intelligence: results of a cross-sectional survey. Int J Med Dev Ctries. 2023;7:811–7.

  29. Chen JS, Baxter SL, van den Brandt A, Lieu A, Camp AS, Do JL, et al. Usability and clinician acceptance of a deep learning-based clinical decision support tool for predicting glaucomatous visual field progression. J Glaucoma. 2023;32:151–8.

    Article  PubMed  Google Scholar 

  30. Constantin A, Atkinson M, Bernabeu MO, Buckmaster F, Dhillon B, McTrusty A, et al. Optometrists’ perspectives regarding artificial intelligence aids and contributing retinal images to a repository: web-based interview study. JMIR Hum Factors. 2023;10:e40887.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Rojas-Carabali W, Sen A, Agarwal A, Tan G, Cheung CY, Rousselot A, et al. Chatbots vs. human experts: evaluating diagnostic performance of chatbots in uveitis and the perspectives on AI adoption in ophthalmology. Ocul Immunol Inflamm. 2024;32:1591–8.

    Article  PubMed  Google Scholar 

  32. Ye T, Xue J, He M, Gu J, Lin H, Xu B, et al. Psychosocial factors affecting artificial intelligence adoption in health care in China: cross-sectional study. J Med Internet Res. 2019;21:e14316.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Schlegl T, Waldstein SM, Bogunovic H, Endstraßer F, Sadeghipour A, Philip AM, et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology. 2018;125:549–58.

    Article  PubMed  Google Scholar 

  34. Yuen V, Ran A, Shi J, Sham K, Yang D, Chan VTT, et al. Deep-learning-based pre-diagnosis assessment module for retinal photographs: a multicenter study. Transl Vis Sci Technol. 2021;10:16.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Faes L, Rosenblatt A, Schwartz R, Touhami S, Ventura CV, Chatziralli IP, et al. Overcoming barriers of retinal care delivery during a pandemic-attitudes and drivers for the implementation of digital health: a global expert survey. Br J Ophthalmol. 2021;105:1738–43.

    Article  PubMed  Google Scholar 

  36. Youssef A, Pencina M, Thakur A, Zhu T, Clifton D, Shah NH. External validation of AI models in health should be replaced with recurring local validation. Nat Med. 2023;29:2686–7.

    Article  CAS  PubMed  Google Scholar 

  37. Ruamviboonsuk P, Chantra S, Seresirikachorn K, Ruamviboonsuk V, Sangroongruangsri S. Economic evaluations of artificial intelligence in ophthalmology. Asia Pac J Ophthalmol. 2021;10:307–16.

    Article  Google Scholar 

  38. Teo ZL, Ting DSW. AI telemedicine screening in ophthalmology: health economic considerations. Lancet Glob Health. 2023;11:e318–e20.

    Article  CAS  PubMed  Google Scholar 

  39. Abdullah YI, Schuman JS, Shabsigh R, Caplan A, Al-Aswad LA. Ethics of artificial intelligence in medicine and ophthalmology. Asia Pac J Ophthalmol. 2021;10:289–98.

    Article  Google Scholar 

  40. Patel MR, Balu S, Pencina MJ. Translating AI for the clinician. JAMA. 2024;332:1701–2.

  41. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digital Med. 2021;4:140.

    Article  Google Scholar 

  42. Joel MZ, Umrao S, Chang E, Choi R, Yang DX, Duncan JS, et al. Using adversarial images to assess the robustness of deep learning models trained on diagnostic images in oncology. JCO Clin Cancer Inform. 2022;6:e2100170.

  43. Jansen BJ, Jung S-g, Salminen J. Employing large language models in survey research. J Nat Lang Process 2023;4:100020.

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Funding

Health and Medical Research Fund, Hong Kong (ref. 19201991).

Author information

Author notes

  1. These authors contributed equally: An Ran Ran, Chun Ho Lui.

Authors and Affiliations

  1. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China

    An Ran Ran, Dawei Yang, Ziqi Tang, Clement C. Tham & Carol Y. Cheung

  2. Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment and Research Centre, The Chinese University of Hong Kong, Hong Kong SAR, China

    An Ran Ran, Clement C. Tham & Carol Y. Cheung

  3. Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China

    Chun Ho Lui, Chiu Yu Lam, Wai Lam Cheung, Siu Ting Chan, Hok Ngai Ma & Raphael Walter L. C. Chow

  4. Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Yih-Chung Tham & Ching-Yu Cheng

  5. Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore

    Yih-Chung Tham & Ching-Yu Cheng

  6. Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore

    Yih-Chung Tham & Ching-Yu Cheng

  7. Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA

    T. Y. Alvin Liu

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Contributions

ARR and CYC initiated the idea. ARR and CHL researched data; performed the systematic search, study selection, data extraction, and wrote the manuscript. CYL, WLC, STC, HNM, and RWLCC contributed to the data synthesis and reviewed and edited the manuscript. Y-CT, C-YC, DWY, ZQT, TYAL, CCT, and CYC contributed to the discussion and reviewed and edited the manuscript. All authors approved the decision to submit for publication. ARR and CYC are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding author

Correspondence to Carol Y. Cheung.

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Ran, A.R., Lui, C.H., Tham, YC. et al. The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis. Eye 39, 2353–2362 (2025). https://doi.org/10.1038/s41433-025-03878-z

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