Evolving Minds: Natural Learning vs. Artificial Learning in Ophthalmology Training

Turkish Journal of Ophthalmology · Şubat 2026

Makale Bilgileri

DergiTurkish Journal of Ophthalmology

Yayın TarihiŞubat 2026

Cilt / Sayfa56 · 1-7

DOI10.4274/tjo.galenos.2025.16517

Scopus ID2-s2.0-105030534628

Erişim🔓 Açık Erişim

Özet Objectives: This study aimed to compare year-over-year change in ChatGPT’s performance on nationwide ophthalmology exams with the performance change among residents over the same period. Materials and Methods: This observational study included ophthalmology residents in Türkiye who participated in both the 2023 and 2024 Resident Training Development Exams organized by the Turkish Ophthalmological Association Qualifications Committee. The 2023 examination consisted of 69 single-best-answer multiple-choice questions and was administered to ChatGPT-3.5. The 2024 version, containing 72 questions, was administered to ChatGPT-4o. The success rates of ChatGPT and the residents who participated in both exams were compared. Results: ChatGPT’s accuracy improved from 53.6% in 2023 to 84.7% in 2024. Among the 501 residents who participated in both years, the average score increased from 48.2% to 53.1%. ChatGPT ranked 292<sup>nd</sup> among residents in 2023 but achieved the top score in 2024. Based on percentage improvement in scores, ChatGPT-4o ranked 8<sup>th</sup> overall. The most notable performance gains for ChatGPT were seen in the areas of strabismus (+75%), neuro-ophthalmology (+40%), and optics (+40%). Among residents, the largest improvement occurred in oculoplastics (+33.5%), while a decrease was observed in cornea and ocular surface (-4.1%). Conclusion: ChatGPT-4o showed a marked improvement in answering ophthalmology questions compared to its predecessor, whereas resident learning progressed more gradually. This rapid advancement in ChatGPT highlights the potential speed with which artificial learning can progress within defined boundaries. In contrast, human learning remains a deeper and more time-intensive process. Results suggest that evolving large language models will play an increasingly significant role in medical education and clinical support.