Is ChatGPT a Useful Tool for Ophthalmology Practice?

Aim: This study aimed to assess ChatGPT-3.5's 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. ChatGPT's 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, ChatGPT's 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 ChatGPT's clarity and acceptability for open-ended questions.

Authors

Institutions

Publication Details

Journal
Acta Medica Alanya
Published
2024-12-29
DOI
https://doi.org/10.30565/medalanya.1531790
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Is ChatGPT a Useful Tool for Ophthalmology Practice?

Fuat Yavrum, Dilara Özkoyuncu Kocabaş
Acta Medica Alanya
Artificial Intelligence in Healthcare and Education
article

Is ChatGPT a Useful Tool for Ophthalmology Practice?

Fuat Yavrum, Dilara Özkoyuncu Kocabaş
article en

Abstract

Aim: This study aimed to assess ChatGPT-3.5's 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. ChatGPT's 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, ChatGPT's 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 ChatGPT's clarity and acceptability for open-ended questions.

Acta Medica AlanyaVol. 8(3)
TOBB University of Economics and Technology (TR), Alanya Alaaddin Keykubat Üniversitesi (TR)
Openalex Percentile: Top 82%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.