Araştırma Makalesi

Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study

Cilt: 36 Sayı: 2026 25 Eylül 2026
PDF İndir
TR EN

Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study

Öz

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.

Anahtar Kelimeler

Artificial intelligence, Medical education, Ophthalmology residency

Destekleyen Kurum

n/a

Proje Numarası

0

Etik Beyan

The study received ethical approval from the Clinical Research Ethics Committee of Selcuk University Faculty of Medicine (Approval No: 2024/496). No additional institutional review boards were involved in the approval process. The research was conducted in full accordance with the ethical standards of the Declaration of Helsinki (1964) and its subsequent amendments. All participants were informed about the purpose, procedures, and voluntary nature of the study on the first page of the questionnaire. Verbal informed consent was obtained from all individuals prior to survey participation. No identifying personal information was collected, and therefore no separate consent for publication of identifiable data was required.

Teşekkür

The authors express gratitude to their colleagues whose voluntary, anonymous involvement in the survey contributed to this study.

Kaynakça

  1. 1. Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP. Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 2020; 9(2):14
  2. 2. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316(22):2402-10
  3. 3. Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial intelligence in medicine: where are we now? Acad Radiol 2020; 27(1):62-70
  4. 4. Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, et al. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023; 4(7)
  5. 5. Gao Z, Jin K, Yan Y, Liu X, Shi Y, Ge Y, et al. End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning. Graefes Arch Clin Exp Ophthalmol 2022; 260(5):1663-73 6. Zhang X, Li F, Li D, Wei Q, Han X, Zhang B, et al. Automated detection of severe diabetic retinopathy using deep learning method. Graefes Arch Clin Exp Ophthalmol 2022; 260(3):849-56
  6. 7. Ting DSW, Cheung CY-L, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 2017; 318(22):2211-23
  7. 8. Li Z, Guo C, Lin D, Nie D, Zhu Y, Chen C, et al. Deep learning for automated glaucomatous optic neuropathy detection from ultra-widefield fundus images. Br J Ophthalmol 2021; 105(11):1548-54
  8. 9. Mastropasqua L, Agnifili L, Brescia L, Figus M, Posarelli C, Oddone F, et al. A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study. Graefes Arch Clin Exp Ophthalmol 2024; 262(1):149-60
  9. 10. Borrelli E, Serafino S, Ricardi F, Coletto A, Neri G, Olivieri C, et al. Deep Learning in Neovascular Age-Related Macular Degeneration. Medicina 2024; 60(6):990
  10. 11. Huo Y, Chen X, Khan GA, Wang Y. Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence. Graefes Arch Clin Exp Ophthalmol 2024; 262(4):1337-49

Kaynak Göster

APA
Okudan, S., Bozkurt Oflaz, A., Acar Duyan, Ş., & Tınkır Kayıtmazbatır, E. (2026). Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study. Genel Tıp Dergisi, 36(2026), 1-6. https://doi.org/10.54005/geneltip.1846508
AMA
1.Okudan S, Bozkurt Oflaz A, Acar Duyan Ş, Tınkır Kayıtmazbatır E. Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study. Genel Tıp Derg. 2026;36(2026):1-6. doi:10.54005/geneltip.1846508
Chicago
Okudan, Süleyman, Ayşe Bozkurt Oflaz, Şule Acar Duyan, ve Emine Tınkır Kayıtmazbatır. 2026. “Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study”. Genel Tıp Dergisi 36 (2026): 1-6. https://doi.org/10.54005/geneltip.1846508.
EndNote
Okudan S, Bozkurt Oflaz A, Acar Duyan Ş, Tınkır Kayıtmazbatır E (01 Eylül 2026) Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study. Genel Tıp Dergisi 36 2026 1–6.
IEEE
[1]S. Okudan, A. Bozkurt Oflaz, Ş. Acar Duyan, ve E. Tınkır Kayıtmazbatır, “Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study”, Genel Tıp Derg, c. 36, sy 2026, ss. 1–6, Eyl. 2026, doi: 10.54005/geneltip.1846508.
ISNAD
Okudan, Süleyman - Bozkurt Oflaz, Ayşe - Acar Duyan, Şule - Tınkır Kayıtmazbatır, Emine. “Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study”. Genel Tıp Dergisi 36/2026 (01 Eylül 2026): 1-6. https://doi.org/10.54005/geneltip.1846508.
JAMA
1.Okudan S, Bozkurt Oflaz A, Acar Duyan Ş, Tınkır Kayıtmazbatır E. Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study. Genel Tıp Derg. 2026;36:1–6.
MLA
Okudan, Süleyman, vd. “Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study”. Genel Tıp Dergisi, c. 36, sy 2026, Eylül 2026, ss. 1-6, doi:10.54005/geneltip.1846508.
Vancouver
1.Süleyman Okudan, Ayşe Bozkurt Oflaz, Şule Acar Duyan, Emine Tınkır Kayıtmazbatır. Perspectives of Ophthalmology Residents on Artificial Intelligence: Results of a Questionnaire-Based Study. Genel Tıp Derg. 01 Eylül 2026;36(2026):1-6. doi:10.54005/geneltip.1846508