ArticleOphthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)2022
Attitudes of optometrists towards artificial intelligence for the diagnosis of retinal disease: A cross-sectional mail-out survey.
Article in Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Diagnostic Risk Prediction Models for Upper Gastrointestinal Cancers: A Systematic Review.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Pooled it
- The implementation challenge of computerised clinical decision support systems for the detection of disease in primary care: systematic review and recommendations.Implementation science : IS · 2025Pooled it
- Healthcare professionals' perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels.BMC health services research · 2025Pooled it
- Working Conditions of Employee Optometrists in Australia.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Attitudes and Knowledge Levels of Optometry Students and Educators Towards Artificial Intelligence in Optometric Practice: An Online Cross-Sectional Survey.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Article
- Hearing health professionals' attitudes and perceived skills toward artificial intelligence.BMC medical education · 2026Article
- The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.Eye (London, England) · 2025Review
- Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.Breast cancer research : BCR · 2025Article
- Barriers to and facilitators of clinician acceptance and use of artificial intelligence in healthcare settings: a scoping review.BMJ open · 2025Article
- Stakeholder Attitudes on AI Integration in Ophthalmology.Klinische Monatsblatter fur Augenheilkunde · 2025Review
- Artificial Intelligence in Optometry: Current and Future Perspectives.Clinical optometry · 2025Review
- Article
- The effectiveness of interventions for optometric referrals into the hospital eye service: A review.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2023Review
- Attitudes of optometrists towards artificial intelligence for the diagnosis of retinal disease: A cross-sectional mail-out survey.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence (AI)-based systems have demonstrated great potential in improving the diagnostic accuracy of retinal disease but are yet to achieve widespread acceptance in routine clinical practice. Clinician attitudes are known to influence implementation. Therefore, this study aimed to identify optometrists' attitudes towards the use of AI to assist in diagnosing retinal disease.
methodsA paper-based survey was designed to assess general attitudes towards AI in diagnosing retinal disease and motivators/barriers for future use. Two clinical scenarios for using AI were evaluated: (1) at the point of care to obtain a diagnostic recommendation, versus (2) after the consultation to provide a second opinion. Relationships between participant characteristics and attitudes towards AI were explored. The survey was mailed to 252 randomly selected practising optometrists across Australia, with repeat mail-outs to non-respondents.
resultsThe response rate was 53% (133/252). Respondents' mean (SD) age was 42.7 (13.3) years, and 44.4% (59/133) identified as female, whilst 1.5% (2/133) identified as gender diverse. The mean number of years practising in primary eye care was 18.8 (13.2) years with 64.7% (86/133) working in an independently owned practice. On average, responding optometrists reported positive attitudes (mean score 4.0 out of 5, SD 0.8) towards using AI as a tool to aid the diagnosis of retinal disease, and would be more likely to use AI if it is proven to increase patient access to healthcare (mean score 4.4 out of 5, SD 0.6). Furthermore, optometrists expressed a statistically significant preference for using AI after the consultation to provide a second opinion rather than during the consultation, at the point-of-care (+0.12, p = 0.01).
conclusionsOptometrists have positive attitudes towards the future use of AI as an aid to diagnose retinal disease. Understanding clinician attitudes and preferences for using AI may help maximise its clinical potential and ensure its successful translation into practice.
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