Work

Perceived Trust in Artificial Intelligence in Eye Care: Demographic Determinants and Variations in Attitudes Among Ophthalmologists and Residents

Year: 2026

Type: article

Abstract: Purpose: To investigate demographic and professional determinants of perceived trust and attitudes toward artificial intelligence (AI) in ophthalmology among ophthalmologists and residents, addressing gaps in understanding factors influencing AI adoption in eye care. Patients and Methods: This cross-sectional study surveyed 156 participants (73.1% female; median age 35 years) from Bulgaria, including specialists (two-thirds) and residents (one-third). A structured questionnaire assessed awareness, trust in AI for diagnostics and therapeutics, expectations, and concerns. Ethical approval was obtained, and informed consent secured. Data were analyzed using chi-square tests for gender differences, Spearman correlations for age, Kruskal–Wallis tests for experience, and thematic analysis for qualitative responses. Results: Significant gender differences included higher self-perceived AI knowledge among males (χ 2 = 35.2, p < 0.001) but lower diagnostic trust (χ 2 = 11.6, p =0.009), with females unanimously rejecting AI as a physician replacement (χ 2 = 54.4, p < 0.001). Older participants perceived greater AI utility in glaucoma care (ρ =0.268, p < 0.001) and anticipated delayed integration (ρ =0.163, p =0.042), while younger ones believed in potential replacement (ρ = – 0.217, p =0.007). Less experienced participants (< 5 years) reported higher awareness (χ 2 = 17.89, p < 0.001) and faster expected integration (χ 2 = 11.29, p =0.010). Residents showed greater awareness and readiness to follow AI recommendations than specialists (p < 0.05). Overall, 64.6% were informed about AI, but trust was low (7.5% for diagnostics); qualitative themes highlighted benefits like diagnostic precision and challenges like regulation gaps. Conclusion: Demographic and professional factors significantly influence AI attitudes in ophthalmology, with limited trust despite optimism. Targeted education and regulatory frameworks are essential to enhance adoption and address variations. Keywords: artificial intelligence, ophthalmology, trust, demographics, artificial intelligence adoption, machine learning in medicine

Source: Clinical ophthalmology

DOI: 10.2147/opth.s557302

Authors: Mladena Radeva, Elitsa Hristova, Rosen Georgiev, Dobrin Boyadzhiev, Zornitsa Zlatarova

Institution: Medical University of Varna

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,320