Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study
Aim: Artificial intelligence (AI) has gained increasing prominence in healthcare, particularly in ophthalmology, where image-based applications are rapidly expanding. This study aimed to evaluate ophthalmology residents’ usage patterns, perceptions, and expectations regarding the integration of AI into clinical practice and residency training.Materials and Methods: A structured, three-part questionnaire was distributed electronically. The first section collected demographic information (age, gender, institution type, and residency year). The second assessed familiarity with and use of AI tools, and the third explored residents’ attitudes, concerns, and expectations related to AI in ophthalmology.Results: A total of 156 ophthalmology residents participated. Of these, 94 (60.3%) were female and 60 (38.5%) male. Nearly all respondents (94.8%) reported prior exposure to AI technologies, and one-quarter expressed strong interest in the field. Most participants supported the integration of AI into residency curricula, with 62 (40.3%) strongly agreeing and 72 (46.8%) somewhat agreeing. Residents aged 20–25 demonstrated significantly greater demand for AI training compared with older age groups (p=0.0008). Subspecialties perceived as most likely to benefit from AI included refractive surgery (69.2%), electrodiagnostics (55.8%), and medical retina (55.1%), whereas vitreoretinal surgery (56.6%), oculoplasty (43.4%), and strabismus (42.8%) were viewed as less amenable to AI integration.Conclusions: Ophthalmology residents exhibit substantial familiarity with and interest in AI and strongly support its incorporation into residency education. While they remain optimistic about AI’s potential, particularly in image-driven subspecialties, they express caution regarding its applicability in surgical fields. These findings underscore the need for structured AI-focused training within ophthalmology residency programs.
Authors
- Ayşe Bozkurt Oflaz (ORCID: https://orcid.org/0000-0001-5894-0220)
- Şule Acar Duyan (ORCID: https://orcid.org/0000-0002-9319-0477)
- Emine Tınkır Kayıtmazbatır (ORCID: https://orcid.org/0000-0002-8553-6992)
- Süleyman Okudan (ORCID: https://orcid.org/0000-0002-9491-1459)
Institutions
- Selçuk University (TR)
Publication Details
- Journal
- Genel Tıp Dergisi
- Published
- 2026-09-25
- DOI
- https://doi.org/10.54005/geneltip.1846508
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00