Turkiye Klinikleri Journal of Ophthalmology

.: ORIGINAL RESEARCH
Evaluation of the Performance of Large Language Models (ChatGPT-3.5, ChatGPT-4, Bing and Bard) in Turkish Ophthalmology Chief-Assistant Exams: A Comparative Study
Göz Hastalıkları Alanında Türkçe Başasistanlık Sınavlarında Geniş Dil Modellerinin (ChatGPT-3.5, ChatGPT-4, Bing ve Bard) Performansların Değerlendirilmesi: Karşılaştırmalı Bir Çalışma
Mehmet CANLEBLEBİCİa , Ali DALb , Murat ERDAĞc
aKayseri State Hospital, Clinic of Ophthalmology, Kayseri, Türkiye
bUğur Eye Hospital, Clinic of Ophthalmology, Kahramanmaraş, Türkiye
cVan Training and Research Hospital, Clinic of Ophthalmology, Van, Türkiye
Turkiye Klinikleri J Ophthalmol. 2024;33(3):163-70
doi: 10.5336/ophthal.2024-102632
Article Language: EN
Full Text
ABSTRACT
 Objective: Recent strides in artificial intelligence, particularly in large language models (LLM), have prompted their exploration in medical education. This study investigates the proficiency of LLMs in Turkish chief-assistant ophthalmology exams, assessing ChatGPT-3.5, ChatGPT4.0, Bing, and Bard. The aim is comparing their accuracy in answering 200 questions spanning six critical ophthalmology topics, providing insights into their potential applications in medical education. Material and Methods: The questions were asked in Turkish and obtained from the chief-assistant exam administered by the Ministry of National Education from internet. A total of 200 questions were presented to each LLM as fallows ChatGPT3.5, ChatGPT-4.0, Bing, and Bard, in October 2023. The questions covered six groups, including Retina and Vitreous as Group-1, Cornea, Cataract and Anterior Segment as Group-2, Glaucoma as Group-3, Pediatric Ophthalmology, Genetics and Clinical Refraction as Group-4, Adnexa, Uvea and Oculoplastic as Group-5, and Neuro-Ophthalmology and Strabismus as Group-6. The primary outcome measure was response accuracy, with topics grouped under these six main headings. Statistical analyses were employed to assess the accuracy and reliability of the responses with Pearson's chi-square test. Results: ChatGPT-4.0 emerges as the most accurate LLM with a 77.5% correct response rate, followed by Bing at 63.0%. In contrast, ChatGPT-3.5 and Bard exhibit lower accuracy at 51% and 45.5%, respectively. Subgroup analyses emphasize ChatGPT-4.0's superiority across all branches, showcasing its efficacy in diverse ophthalmology topics. Conclusion: Despite promising results, the study acknowledges challenges in accuracy and underscores the imperative for continual improvements in LLMs, especially in the realm of clinical applications and education.

Keywords: Artificial intelligence; education and training; large language models; ophthalmology
ÖZET
 Amaç: Yapay zekâ alanında, özellikle de büyük dil modellerindeki [large language models (LLM)] son gelişmeler, bunların tıp eğitiminde araştırılmasına yol açmıştır. Bu çalışma, ChatGPT-3.5, ChatGPT-4.0, Bing ve Bard'ı değerlendirerek, LLM'lerin Türkçe başasistan oftalmoloji sınavlarındaki yeterliliğini araştırmaktadır. Çalışmanın amacı, 6 ana oftalmoloji konusunu kapsayan 200 soruyu yanıtlamadaki doğruluk oranlarını karşılaştırmak ve tıp eğitimindeki potansiyel uygulamalarına ilişkin alanları tartışmaktır. Gereç ve Yöntemler: Araştırmada kullanılmak üzere, önceki yıllarda Millî Eğitim Bakanlığı tarafından uygulanan ve Türkçe sorulan başasistanlık sınavı soruları internet üzerinden elde edildi. Toplam 200 soru Ekim 2023'te her bir LLM'ye ChatGPT-3.5, ChatGPT-4.0, Bing ve Bard olmak üzere tek tek sunulmuştur. Sorular, Grup 1 olarak Retina ve Vitreus, Grup 2 olarak Kornea, Katarakt ve Ön Segment, Grup 3 olarak Glokom, Grup 4 olarak Pediatrik Oftalmoloji, Genetik ve Klinik Refraksiyon, Grup 5 olarak Adneksa, Uvea ve Oküloplastik ve Grup 6 olarak Nöro-Oftalmoloji ve Şaşılık olmak üzere 6 grubu kapsamaktadır. Birincil değerlendirme ölçütü, bu 6 ana başlık altında gruplandırılan konularla birlikte doğru yanıtlama oranıdır. Yanıtların doğruluğunu ve güvenilirliğini değerlendirmek için Pearson'ın ki-kare testi ile istatistiksel analizler yapılmıştır. Bulgular: ChatGPT-4.0 %77,5 doğruluk oranıyla en iyi performansı gösteren LLM olmuştur ve onu %63,0 ile Bing takip etmektedir. Buna karşılık, ChatGPT-3.5 ve Bard sırasıyla %51 ve %45,5 ile daha düşük doğruluk oranı sergilemektedir. Alt grup analizleri ChatGPT-4.0'ın tüm branşlardaki üstünlüğünü vurgulayarak çeşitli oftalmoloji konularındaki etkinliğini ortaya koymaktadır. Sonuç: Umut verici sonuçlara rağmen bu çalışma doğruluk konusundaki sorunları göstermekte ve özellikle eğitim ve klinik uygulamalar alanında LLM'lerde sürekli iyileştirmeler yapılması zorunluluğunun altını çizmektedir.

Anahtar Kelimeler: Yapay zekâ; eğitim ve öğretim; geniş dil modelleri; oftalmoloji
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