Is ChatGPT a Useful Tool for Ophthalmology Practice?

Fuat Yavrum; Dilara Özkoyuncu

Makale detayları

Is ChatGPT a Useful Tool for Ophthalmology Practice?

Yazarlar:

Fuat Yavrum | Dilara Özkoyuncu

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Klinik Tıp Bilimleri (Diğer)

Dergi:

Acta Medica Alanya Vol.8 Issue 3, pp.221-227

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337179

pISSN:

2587-0319

Anahtar kelimeler:

Özet:

 Aim: This study aimed to assess ChatGPT-3.5s performance in ophthalmology, comparing its responses to clinical case-based and multiple-choice MCQ questions. Methods: ChatGPT-3.5, an AI model developed by OpenAI, was employed. It responded to 98 case-based questions from Ophthalmology Review: A Case-Study Approach and 643 MCQs from Review Questions in Ophthalmology book. ChatGPTs answers were compared to the books, and statistical analysis was conducted. Results: ChatGPT achieved an overall accuracy of 56.1 in case-based questions. Accuracy varied across categories, with the highest in the retina section 69.5 and the lowest in the trauma section 38.2. In MCQ, ChatGPTs accuracy was 53.5, with the weakest in the optics section 32.6 and the highest in pathology and uveitis 66.7 and 63.0, respectively. ChatGPT performed better in case-based questions in the retina and pediatric ophthalmology sections than MCQ. Conclusion: ChatGPT-3.5 exhibits potential as a tool in ophthalmology, particularly in retina and pediatric ophthalmology. Further research is needed to evaluate ChatGPTs clarity and acceptability for open-ended questions.

 Aim: This study aimed to assess ChatGPT-3.5s performance in ophthalmology, comparing its responses to clinical case-based and multiple-choice MCQ questions. Methods: ChatGPT-3.5, an AI model developed by OpenAI, was employed. It responded to 98 case-based questions from Ophthalmology Review: A Case-Study Approach and 643 MCQs from Review Questions in Ophthalmology book. ChatGPTs answers were compared to the books, and statistical analysis was conducted. Results: ChatGPT achieved an overall accuracy of 56.1 in case-based questions. Accuracy varied across categories, with the highest in the retina section 69.5 and the lowest in the trauma section 38.2. In MCQ, ChatGPTs accuracy was 53.5, with the weakest in the optics section 32.6 and the highest in pathology and uveitis 66.7 and 63.0, respectively. ChatGPT performed better in case-based questions in the retina and pediatric ophthalmology sections than MCQ. Conclusion: ChatGPT-3.5 exhibits potential as a tool in ophthalmology, particularly in retina and pediatric ophthalmology. Further research is needed to evaluate ChatGPTs clarity and acceptability for open-ended questions.

 Aim: This study aimed to assess ChatGPT-3.5s performance in ophthalmology, comparing its responses to clinical case-based and multiple-choice MCQ questions. Methods: ChatGPT-3.5, an AI model developed by OpenAI, was employed. It responded to 98 case-based questions from Ophthalmology Review: A Case-Study Approach and 643 MCQs from Review Questions in Ophthalmology book. ChatGPTs answers were compared to the books, and statistical analysis was conducted. Results: ChatGPT achieved an overall accuracy of 56.1 in case-based questions. Accuracy varied across categories, with the highest in the retina section 69.5 and the lowest in the trauma section 38.2. In MCQ, ChatGPTs accuracy was 53.5, with the weakest in the optics section 32.6 and the highest in pathology and uveitis 66.7 and 63.0, respectively. ChatGPT performed better in case-based questions in the retina and pediatric ophthalmology sections than MCQ. Conclusion: ChatGPT-3.5 exhibits potential as a tool in ophthalmology, particularly in retina and pediatric ophthalmology. Further research is needed to evaluate ChatGPTs clarity and acceptability for open-ended questions.

 Amaç: ChatGPT-3.5in performansını göz hastalıkları alanında değerlendirmek, klinik vaka bazlı sorular ve çoktan seçmeli sorulara ÇSS verdiği yanıtların doğruluk oranını karşılaştırmaktır. Yöntem: Çalışmada OpenAI tarafından geliştirilen bir yapay zeka modeli olan ChatGPT-3.5 kullanıldı. Modelden, Ophthalmology Review: A Case-Study Approach kitabından 98 vaka bazlı soruya ve Review Questions in Ophthalmology kitabından 643 ÇSSye yanıt vermesi istendi. ChatGPTnin cevapları kitaplarla karşılaştırıldı ve istatistiksel analizi yapıldı. Bulgular: ChatGPT, vaka bazlı sorularda genel olarak 56,1 doğruluk oranı gösterdi.. Doğruluk oranı kategoriler arasında en yüksek retina bölümünde 69,5 ve en düşük travma bölümünde 38,2 idi. ÇSSde ChatGPTnin genel doğruluk oranı 53,5 olarak gözlendi, bunların en düşüğü optik bölümünde 32,6 ve en yükseği patoloji ve üveit bölümlerinde 66,7 ve 63 idi. ChatGPT özellikle retina ve pediatrik oftalmoloji bölümlerindeki vaka bazlı sorularda ÇSSye kıyasla daha iyi performans gösterdi. Sonuç: ChatGPT-3.5, özellikle retina ve pediatrik oftalmoloji alanlarında göz hastalıkları için potansiyel bir yardımcı araç olarak görülmektedir. ChatGPTnin açık uçlu sorular için netlik ve kabul edilebilirliğini değerlendirmek için daha fazla araştırma yapılması gerekmektedir.

Makale detayları

Is ChatGPT a Useful Tool for Ophthalmology Practice?

Yazarlar:

Fuat Yavrum | Dilara Özkoyuncu

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Klinik Tıp Bilimleri (Diğer)

Dergi:

Acta Medica Alanya Vol.8 Issue 3, pp.221-227

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337179

pISSN:

2587-0319

Artificial intelligence

ChatGPT

Large language model

OphthalmologyYapay zeka

ChatGPT

Büyük dil modeli

Oftalmoloji

Sayı

Makale detayları

Yazarlar: Fuat Yavrum | Dilara Özkoyuncu

Yayıncı: Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar: Klinik Tıp Bilimleri (Diğer)

Dergi: Acta Medica Alanya Vol.8 Issue 3, pp.221-227

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001337179

pISSN: 2587-0319

Sayı

Yazarlar:

Fuat Yavrum | Dilara Özkoyuncu

Yayıncı:

Alanya Alaaddin Keykubat Üniversitesi

Ana Başlıklar:

Klinik Tıp Bilimleri (Diğer)

Dergi:

Acta Medica Alanya Vol.8 Issue 3, pp.221-227

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001337179

pISSN:

2587-0319