AccScience Publishing / AIH / Volume 1 / Issue 2 / DOI: 10.36922/aih.2809
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The perspectives of eye care professionals on the integration of artificial intelligence in eye care practices: A systematic review

Obehi Suzan Idogen1*
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1 Department of Optometry, Faculty of Life Sciences, University of Benin, Benin, Edo, Nigeria
AIH 2024, 1(2), 66–75;
Submitted: 23 January 2024 | Accepted: 6 March 2024 | Published: 22 April 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Artificial intelligence (AI) technology has recently been integrated into the health-care industry, including in optometry and ophthalmology. This systematic review assessed the opinions (i.e., perspectives, concerns, and degrees of acceptance) of eye care professionals regarding AI integration into eye care practices. The literature search was conducted using the PubMed and MEDLINE databases. A total of 780 related articles were identified. Among these articles, 304 duplicates were removed, 450 articles were excluded after reviewing the abstract, and 18 articles were excluded after reviewing the full text as these articles were not relevant and/or did not report surveys. The remaining eight included studies were assessed accordingly. Most ophthalmologists and optometrists had a positive perception toward incorporating AI into eye care practices, and these professionals shared that AI would effectively enhance clinical eye care practices. However, certain eye care professionals were concerned about the diagnostic accuracy of AI, the high implementation costs, privacy issues, and the quality of AI-integrated patient care. Several eye care professionals also expressed concerns that AI technology could eventually replace some of their major responsibilities in the practice, suggesting that stakeholders should essentially address these concerns and ensure that AI integration in eye care practices is implemented thoughtfully and ethically to maximize its benefits while preserving the quality of patient care. Nonetheless, this systematic review highlighted the predominantly positive attitude among eye care professionals toward AI integration into eye care practices, warranting further research and collaboration between AI developers and eye care professionals to effectively address the current challenges.

Artificial intelligence
Perspective of ophthalmologists
Perspective of optometrists
Eye care
  1. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e194. doi: 10.7861/fhj.2021-0095


  1. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689. doi: 10.1186/s12909-023-04698-z


  1. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94-98. doi: 10.7861/futurehosp.6-2-94


  1. Gruson D. L’intelligence artificielle en santé, un potentiel majeur d’innovations pour notre système de santé Artificial intelligence in healthcare: Major potential for innovations in our health system]. Soins. 2019;64(838):33-35. doi: 10.1016/j.soin.2019.06.006


  1. Madison DE. Rapid prototyping for healthcare applications. Comput Healthc. 1989;10(11):35-38.


  1. Chan Y, Chen Y, Pham T, Chang W, Hsieh MY. Artificial intelligence in medical applications. J Healthc Eng. 2018;2018:4827875. doi: 10.1155/2018/4827875


  1. Basu K, Sinha R, Ong A, Basu T. Artificial intelligence: How is it changing medical sciences and its future? Indian J Dermatol. 2020;65(5):365-370. doi: 10.4103/ijd.IJD_421_20.


  1. Yu YY. Role of artificial intelligence in the diagnosis and treatment of gastrointestinal diseases. Zhonghua Wei Chang Wai Ke Za Zhi. 2020;23(1):33-37. doi: 10.3760/cma.j.issn.1671-0274.2020.01.006


  1. Niel O, Bastard P. Artificial intelligence in nephrology: Core concepts, clinical applications, and perspectives. Am J Kidney Dis. 2019;74(6):803-810. doi: 10.1053/j.ajkd.2019.05.020


  1. Bitkina OV, Park J, Kim HK. Application of artificial intelligence in medical technologies: A systematic review of main trends. Digit Health. 2023;9:20552076231189331. doi: 10.1177/20552076231189331


  1. Yang X, Wu J, Chen X. Application of artificial intelligence to the diagnosis and therapy of nasopharyngeal carcinoma. J Clin Med. 2023;12(9):3077. doi: 10.3390/jcm12093077


  1. Faizal KD, Sultan RF. Applications of artificial intelligence and big data analytics in m-health: A healthcare system perspective. J Healthc Eng. 2020;2020:8894694. doi: 10.1155/2020/8894694


  1. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-731. doi: 10.1038/s41551-018-0305-z


  1. Manickam P, Mariappan SA, Murugesan SM, et al. Artificial intelligence (AI) and internet of medical things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors (Basel). 2022;12(8):562. doi: 10.3390/bios12080562


  1. Playford D, Bordin E, Mohamad R, Stewart S, Strange G. Enhanced diagnosis of severe aortic stenosis using artificial intelligence: A proof-of-concept study of 530,871 echocardiograms. JACC Cardiovasc. Imaging. 2020;13(4):1087-1090. doi: 10.1016/j.jcmg.2019.10.013


  1. Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y. Applications of artificial intelligence in ophthalmology: General overview. J Ophthalmol. 2018;2018:5278196. doi: 10.1155/2018/5278196


  1. Tan Z, Scheetz J, He M. Artificial intelligence in ophthalmology: Accuracy, challenges, and clinical application. Asia Pac J Ophthalmol (Phila). 2019;8(3):197-199. doi: 10.22608/APO.2019122


  1. Bellemo V, Lim ZW, Lim G, et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: A clinical validation study. Lancet Digit Health. 2019;1(1):e35-e44. doi: 10.1016/S2589-7500(19)30004-4


  1. Shao Y, Jie Y, Liu ZG, et al. Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023). Int J Ophthalmol. 2023;16(9):1373-1385. doi: 10.18240/ijo.2023.09.03


  1. Krishnan G, Singh S, Pathania M, et al. Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm. Front Artif Intell. 2023;6:1227091. doi: 10.3389/frai.2023.1227091


  1. Yoon JH, Pinsky MR, Clermont G. Artificial intelligence in critical care medicine. Crit Care. 2022;26(1):75. doi: 10.1186/s13054-022-03915-3


  1. Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017;2(4):230-243. doi: 10.1136/svn-2017-000101


  1. Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: Intelligent imaging. J Med Imaging Radiat Sci. 2019;50(4):477-487. doi: 10.1016/j.jmir.2019.09.005


  1. Al-Atari MA. Artificial intelligence for medical diagnostics-existing and future AI technology!. Diagnostics (Basel). 2023;13(4):688. doi: 10.3390/diagnostics13040688


  1. Miller DD, Brown EW. How cognitive machines can augment medical imaging. AJR Am J Roentgenol. 2019;212(1):9-14. doi: 10.2214/AJR.18.19914


  1. Li H, Cao J, Grzybowski A, Jin K, Lou L, Ye J. Diagnosing systemic disorders with AI algorithms based on ocular images. Healthcare (Basel). 2023;11(12):1739. doi: 10.3390/healthcare11121739


  1. Tan Y, Sun X. Ocular images-based artificial intelligence on systemic diseases. Biomed Eng Online. 2023;22(1):49. doi: 10.1186/s12938-023-01110-1


  1. Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol (Phila). 2019;8(3):264-272. doi: 10.22608/APO.2018479


  1. Li H, Cao J, You K, Zhang Y, Ye J. Artificial intelligence-assisted management of retinal detachment from ultra-widefield fundus images based on weakly-supervised approach. Front Med (Lausanne). 2024;11:1326004. doi: 10.3389/fmed.2024.1326004


  1. Pinto-Coelho L. How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications. Bioengineering (Basel). 2023;10(12):1435. doi: 10.3390/bioengineering10121435


  1. Zirar A, Ali IS, Islam M. Worker and workplace artificial intelligence (AI) coexistence: Emerging themes and research agenda. Technovation. 2023;124(1):102747. doi: 10.1016/j.technovation.2023.102747


  1. González-Gonzalo C, Thee EF, Klaver CCW, et al. Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice. Prog Retin Eye Res. 2022;90:101034. doi: 10.1016/j.preteyeres.2021.101034


  1. Du XL, Li WB, Hu BJ. Application of artificial intelligence in ophthalmology. Int J Ophthalmol. 2018;11(9):1555-1561. doi: 10.18240/ijo.2018.09.21


  1. Du HQ, Dai Q, Zhang ZH, et al. Artificial intelligence-aided diagnosis and treatment in the field of optometry. Int J Ophthalmol. 2023;16(9):1406-1416. doi: 10.18240/ijo.2023.09.06


  1. Peterson L, Larsson I, Nygren JM, et al. Challenges to implementing artificial intelligence in healthcare: A qualitative interview study with healthcare leaders in Sweden. BMC Health Serv Res. 2022;22(1):850. doi: 10.1186/s12913-022-08215-8


  1. Cobelli N, Cassia F, Burro R. Factors affecting the choices of adoption/non-adoption of future technologies during coronavirus pandemic. Technol Forecast Soc Change. 2021;169:120814. doi: 10.1016/j.techfore.2021.120814


  1. Jedwab RM, Hutchinson AM, Manias E, et al. Nurse motivation, engagement and well-being before an electronic medical record system implementation: A mixed methods study. Int J Environ Res Public Health. 2021;18(5):2726. doi: 10.3390/ijerph18052726


  1. Scanzera AC, Shorter E, Kinnaird C, et al. Optometrist’s perspectives of artificial intelligence in eye care. J Optom. 2022;15 Suppl 1(Suppl 1):S91-S97. doi: 10.1016/j.optom.2022.06.006


  1. Gunasekeran DV, Zheng F, Lim GYS, et al. Acceptance and perception of artificial intelligence usability in eye care (APPRAISE) for ophthalmologists: A multinational perspective. Front Med. 2022;9:875242. doi: 10.3389/fmed.2022.875242


  1. 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(6):1170-1179. doi: 10.1111/opo.13034


  1. Constantin A, Atkinson M, Bernabeu MO, 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. doi: 10.2196/40887


  1. Al-Khaled T, Valikodath N, Cole E, et al. Evaluation of physician perspectives of artificial intelligence in ophthalmology: A pilot study. Invest Ophthalmol Vis Sci. 2020;61(7):202.


  1. Scheetz J, Rothschild P, McGuinness M, et al. A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology. Sci Rep. 2021;11(1):5193. doi: 10.1038/s41598-021-84698-5


  1. Valikodath NG, Al-Khaled T, Cole E, et al. Evaluation of pediatric ophthalmologists’ perspectives of artificial intelligence in ophthalmology. J AAPOS. 2021;25(3):164. e1-164.e5. doi: 10.1016/j.jaapos.2021.01.011


  1. Alwadani AF, Zakaria MO, Alwadany MN, et al. Ophthalmologists’ view of artificial intelligence: Results of a cross-sectional survey. Int J Med Dev Countries. 2023;7(5):811-817. doi: 10.24911/IJMDC.51-1673725204


  1. Islam MM, Poly TN, Li YJ. Recent advancement of clinical information systems: Opportunities and challenges. Yearb Med Inform. 2018;27(1):83-90. doi: 10.1055/s-0038-1667075


  1. Sauerbrei A, Kerasidou A, Lucivero F, Hallowell N. The impact of artificial intelligence on the person-centred, doctor-patient relationship: Some problems and solutions. BMC Med Inform Decis Mak. 2023;23(1):73. doi: 10.1186/s12911-023-02162-y


  1. Kerasidou A. Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare. Bull World Health Organ. 2020;98(4):245-250. doi: 10.2471/BLT.19.237198


  1. Hogg HDJ, Al-Zubaidy M, Talks J, et al. Stakeholder perspectives of clinical artificial intelligence implementation: Systematic review of qualitative evidence. J Med Internet Res. 2023;25:e39742. doi: 10.2196/39742


  1. Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: A qualitative survey study. Front Artif Intell. 2020;3:578983. doi: 10.3389/frai.2020.578983


  1. Orlova IA, Akopyan ZA, Plisyuk A, et al. Opinion research among Russian Physicians on the application of technologies using artificial intelligence in the field of medicine and health care. BMC Health Serv Res. 2023;23(1):749. doi: 10.1186/s12913-023-09493-6


  1. Tan TF, Thirunavukarasu AJ, Jin L, et al. Artificial intelligence and digital health in global eye health: Opportunities and challenges. Lancet Glob Health. 2023;11(9):e1432-e1443. doi: 10.1016/S2214-109X(23)00323-6


  1. Li JO, Liu H, Ting DSJ, et al. Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. Prog Retin Eye Res. 2021;82:100900. doi: 10.1016/j.preteyeres.2020.100900


  1. Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. Adv Ophthalmol Pract Res. 2022;2(3):100078. doi: 10.1016/j.aopr.2022.100078


  1. Balogh EP, Miller BT, Ball JR, editors. Improving Diagnosis in Health Care. United States: National Academies Press (US); 2015.


  1. Croskerry P. Perspectives on diagnostic failure and patient safety. Healthc Q. 2012;15 Spec No:50-56. doi: 10.12927/hcq.2012.22841


  1. Ruamviboonsuk P, Chantry S, Seresirikachorn K, Ruamviboonsuk V, Sangroongruangsri S. Economic evaluations of artificial intelligence in ophthalmology. Asia Pac J Ophthalmol (Phila). 2021;10(3):307-316. doi: 10.1097/APO.0000000000000403


  1. Gray BH, editors. Institute of Medicine (US). Committee on Implications of For-Profit Enterprise in Health Care. United States: National Academies Press (US); 1986.


  1. Institute of Medicine (US) Committee on regional health data networks. In: Donaldson MS, Lohr KN, editors. Health Data in the Information Age: Use, Disclosure, and Privacy. United States: National Academies Press (US); 1994.


  1. Tegegne MD, Melaku MS, Shimie AW, et al. Health professionals’ knowledge and attitude towards patient confidentiality and associated factors in a resource-limited setting: A cross-sectional study. BMC Med Ethics. 2022;23(1):26. doi: 10.1186/s12910-022-00765-0


  1. Moudatsou M, Stavropoulou A, Philalithis A, Koukouli S. The role of empathy in health and social care professionals. Healthcare (Basel). 2020;8(1):26. doi: 10.3390/healthcare8010026


  1. McNulty JP, Politis Y. Empathy, emotional intelligence and interprofessional skills in healthcare education. J Med Imaging Radiat Sci. 2023;54(2):238-246. doi: 10.1016/j.jmir.2023.02.014


  1. Murdoch B. Privacy and artificial intelligence: Challenges for protecting health information in a new era. BMC Med Ethics. 2021;22(1):122. doi: 10.1186/s12910-021-00687-3


  1. Pashkov VM, Harkusha AO, Harkusha YO. Artificial intelligence in medical practice: Regulative issues and perspectives. Wiad Lek. 2020;73(12 cz 2):2722-2727.


  1. Morrow E, Zidaru T, Ross F, et al. Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Front Psychol. 2023;13:971044. doi: 10.3389/fpsyg.2022.971044


  1. Wang C, Zhang J, Lassi N, Zhang X. Privacy protection in using artificial intelligence for healthcare: Chinese regulation in comparative perspective. Healthcare (Basel). 2022;10(10):1878. doi: 10.3390/healthcare10101878


  1. Page MJ, McKenzie JEM, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71
Conflict of interest
The author declares no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Published by AccScience Publishing