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RCR Global AI Conference 2026
29-30 June, 2026 | QEII Centre, Westminster
63
<p>Artificial Intelligence in Eye Care Practice: A Qualitative Multi-Disciplinary Perspective.</p>
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Risk management and compliance: projects around governance, compliance and/or ethics and societal impact
Title: Artificial Intelligence in Eye Care Practice: A Qualitative Multi-Disciplinary Perspective.
Authors: Fiona Buckmaster, MSc; Diane van Staden, PhD; Lauren Coetzee, PhD.
Purpose:
The purpose of this study was to explore a wide range of stakeholder perspectives towards the use of artificial intelligence (AI) in eye care.
Background:
• AI technologies are increasingly being developed for use in eye care (1).
• Successful implementation of AI tools in eye care relies on the technology being accepted by key stakeholders (2).
• Previous literature exploring attitudes towards AI in eye care (2) has primarily focused on the perspectives of eye care practitioners (ECPs).
Methods:
• Semi-structured interviews (n = 21) were conducted with a purposive sample of ECPs (n = 5), optometry students (n = 5), optometry educators (n = 5), AI experts (n = 5), and an optometry regulator (n = 1).
• A total of 15 hours and 13 minutes of interviews were conducted online, recorded and transcribed verbatim. Transcriptions were confirmed for accuracy by participants prior to analysis.
• Personal identifiers were removed from the data and participants’ names replaced with numbered codes A, E, P, R and S denoting AI specialists, educators, ECPs, regulators, and students, respectively.
• Transcripts were analysed using inductive thematic analysis.
Themes and supporting statements:
Integration of AI into eye care practice: “AI can’t feel emotions. We can. We can sympathise. So, I think that plays a very big role.” - S2, Optometry Student. “…if and when AI becomes more integrated into diagnostic technology, there's no avoiding AI. And that's not up to the practitioner, that's up to the manufacturer.” - E5, Senior Lecturer in Optometry.
Developing AI for use in eye care: “[AI companies] talk about being disruptive because it sounds cool and exciting, but that is not what healthcare people want. They don’t want disruption.” - A4, Principal Investigator. “For AI development, actually optometry data is super useful… You should try to have data that reflects the intended use case.” - A5, Post-Doc Researcher.
Trust, guidance and ethical considerations: “You will need the optometrist to have the final say… You can’t hold a machine accountable.” - A2, Senior Lecturer in Computing. “I think you would have to have experience using [an AI tool] in order to gain trust... And I would be interested in reading the data on it before starting to use it.” - P4, Clinical Governance Optometrist.
Potential uses of AI in eye care:
• Screening and examining: screening (n = 9), refraction (n = 2), visual fields (n = 2), spectacle dispensing (n = 1).
• Diagnosis/decision making: image analysis (n = 13), diagnostic assistance (n = 9), improved decision making (n = 5), referrals and triage (n = 5), oculomics (n = 1).
• Practice management: administrative tasks (n = 6), history taking or record keeping (n = 4).
• Patient's health at home: telehealth (n = 3), treatment compliance (n = 2), at-home monitoring (n = 1).
• Prevention and prediction: predicting progression (n = 2), preventative healthcare (n = 1), clinical trials (n = 1).
Conclusions:
• There are a broad range of views on the use of AI in eye care practice.
• Participants saw the potential for both positive and negative impacts from AI technology developments.
• Potential uses for AI tools were identified across the entire patient journey, with image analysis being the most commonly identified use.
• While early guidance on the use of AI in eye care has been issued, further guidance and training is required for practitioners to safely and effectively utilise AI tools in their future practice.
References:
1.Krishnan, A., et al. (2025) ‘Artificial Intelligence in Optometry: Current and Future Perspectives.’ Clinical Optometry, 17, 83 – 114.
2.Ran, AR, et al. (2025) ‘The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.’ Eye, 39, 2353 – 2362.