Assessment of ophthalmology residents' perspectives on the utilization of artificial intelligence in contemporary practices

Aims/Purpose: To survey ophthalmology residents on their perspectives of artificial intelligence (AI) and their implications regarding its potential use in ophthalmology. Methods: Online anonymous questionnaire sent out via e‐mail toTunisian ophthalmology residents from all different educational levels (1st to 5th year) between May and June 2024. The survey contained 18items, assessing the extent participants either "Yes" or "No" or multiple choice with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics. Data were analyzed using SPSS 26.0 Results: Thirty ophthalmology residents were included. Of the participants, 70% were female,and 30% were male. The mean age was 28.26 years ± 3 years. Among the participants, 73.3% consider themselves to have enough potentials to understand AI, and 86.7% recognize the importance of adapting to AI in ophthalmology. According to Tunisian ophthalmology residents, AI most important applications in this field include diabetic retinopathy (30%) and optical coherence tomography angiography (26.7%). Eight residents thought that AI can improve the practice of ophthalmology by 50%. All participants agreed on that AI could save time for patients; 96.7% believed AI cannot replace physicians. For83.3% of residents, considering learning AI basics ought to be mandatory during the medical curriculum, as 90 % believed that this tool can help making more accurate diagnosis; 93.3% believed it may reduce consultation time for residents, and 90 % agreed it may help in management and treatment prescription. More than ¾ tried to adapt AI in clinical practice and almost 97% of residents are willing to adapt AI to facilitate research. Conclusions: Learning and applying AI for clinical practice may be a promising technology for ophthalmology residents, in order to save time, facilitate diagnosis and management. Moreover, Tunisian residents seem to be willing to improve their competences in this field and adapt this tool for research and medical publications. Reference Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019 Feb; 103(2): 167‐175.