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Updated: Jul 2, 2026

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Large Language Models for Ophthalmology Training in China: A Prospective Evaluation.

Zuhui Zhang1, Changke Huang1, Xinxin Yu1

  • 1National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China.

Ophthalmology Science
|July 1, 2026
PubMed
Summary

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Translational vision science & technology·2024

Large language models (LLMs) show promise in ophthalmology training, significantly improving resident performance on text-based exams. However, LLMs struggle with image diagnostics, highlighting potential risks in AI-assisted medical education.

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Education

Background:

  • Global shortage and uneven distribution of ophthalmologists necessitate innovative training solutions.
  • Large Language Models (LLMs) offer potential for scalable medical education.
  • Evaluating LLM effectiveness and risks in ophthalmic training is crucial.

Purpose of the Study:

  • To assess the effectiveness of LLMs in ophthalmic training.
  • To explore the potential risks associated with LLM use in ophthalmology.
  • To investigate LLMs as a solution for ophthalmologist shortages.

Main Methods:

  • Prospective study involving 11 LLMs and 10 resident physicians (RPs).
  • LLMs tested on Chinese and English ophthalmology qualification exams (CNHPTQE-O).
Keywords:
Artificial ignorance.Artificial intelligenceLarge language modelsOphthalmology trainingResident physicians

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  • Best-performing LLM assisted RPs in text-based exams and keratitis image classification.

    • Main Results:

    • Chinese LLMs, particularly ERNIE Bot 4.5 Turbo, excelled in CNHPTQE-O exams (98.00% Chinese, 86.50% English).
    • LLM assistance improved RP accuracy on text exams from 60.75% to 79.00% (P = 0.001).
    • LLM assistance did not improve RP accuracy in keratitis image classification (41.25% vs. 40.56%, P = 0.662).

    Conclusions:

    • LLMs demonstrate strong ophthalmic knowledge and potential as text-based training aids.
    • LLM limitations exist in image-assisted diagnostics, posing risks of "artificial ignorance".
    • Careful integration of LLMs is needed to maximize benefits and mitigate risks in ophthalmic training.