Semantic Scholar Open Access 2023 444 sitasi

Evaluating the Performance of ChatGPT in Ophthalmology

F. Antaki Samir Touma D. Milad J. El-Khoury R. Duval

Lihat Sumber DOI

Abstrak

We tested the accuracy of ChatGPT, a large language model (LLM), in the ophthalmology question-answering space using two popular multiple choice question banks used for the high-stakes Ophthalmic Knowledge Assessment Program (OKAP) exam. The testing sets were of easy-to-moderate difficulty and were diversified, including recall, interpretation, practical and clinical decision-making problems. ChatGPT achieved 55.8% and 42.7% accuracy in the two 260-question simulated exams. Its performance varied across subspecialties, with the best results in general medicine and the worst in neuro-ophthalmology and ophthalmic pathology and intraocular tumors. These results are encouraging but suggest that specialising LLMs through domain-specific pre-training may be necessary to improve their performance in ophthalmic subspecialties.

Topik & Kata Kunci

Medicine

Penulis (5)

F. Antaki

Samir Touma

D. Milad

J. El-Khoury

R. Duval

Format Sitasi

APA MLA BibTeX

Antaki, F., Touma, S., Milad, D., El-Khoury, J., Duval, R. (2023). Evaluating the Performance of ChatGPT in Ophthalmology. https://doi.org/10.1101/2023.01.22.23284882

Akses Cepat

Lihat di Sumber doi.org/10.1101/2023.01.22.23284882

Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Total Sitasi: 444×
Sumber Database: Semantic Scholar
DOI: 10.1101/2023.01.22.23284882
Akses: Open Access ✓