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Comparative Assessment of Large Language Models in Optics and Refractive Surgery: Performance on Multiple-Choice

Leah Attal^1,2, Elad Shvartz^1,3, Alon Gorenshtein^1,4,5

¹Azrieli Faculty of Medicine, Bar Ilan University, Ramat-Gan 5290002, Israel.

Vision (Basel, Switzerland)

|October 24, 2025

Summary

This summary is machine-generated.

Seven advanced AI Large Language Models (LLMs) were tested on ophthalmology certification exams. ChatGPT O1 excelled overall and in calculations, while DeepSeek V3 and ChatGPT O3 Mini led in refractive surgery and image analysis, respectively.

Keywords:

ChatGPT artificial intelligence large language models medical education multiple-choice questions optics

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Area of Science:

Ophthalmology
Artificial Intelligence
Medical Education

Background:

Large Language Models (LLMs) show promise in various fields.
Assessing LLM performance in specialized medical domains like ophthalmology is crucial.
Understanding LLM capabilities can inform their integration into medical training.

Purpose of the Study:

To evaluate the accuracy of seven advanced AI Large Language Models (LLMs) on multiple-choice questions (MCQs) in optics and refractive surgery.
To compare the performance of LLMs based on question type (calculations, subspecialty, image-based).
To explore the potential role of LLMs in ophthalmology residency education.

Main Methods:

Seven LLMs (ChatGPT 4o, ChatGPT O3 Mini, ChatGPT O1, DeepSeek V3, DeepSeek R1, Gemini 2.0 Flash, Grok-3) were tested.
134 publicly available MCQs from national ophthalmology certification exams were used.
Accuracy was statistically analyzed and compared across models and question categories.

Main Results:

ChatGPT O1 achieved the highest overall accuracy (83.5%), excelling in optical calculations (84.1%) and optics questions (82.4%).
DeepSeek V3 (89.7%) and ChatGPT O3 Mini (88.4%) showed superior accuracy in refractive surgery questions.
ChatGPT O3 Mini led in image analysis (88.2%), and ChatGPT O1 demonstrated consistent accuracy for calculated and non-calculated questions.

Conclusions:

Advanced LLMs demonstrate high accuracy in ophthalmology MCQs, including complex calculations and visual items.
LLMs show potential for exam preparation and medical training in ophthalmology.
Further research with larger, multilingual datasets is needed to confirm and extend these findings for educational applications.