Comparative Evaluation of Deep-Reasoning Large Language Models for Ophthalmic Emergencies

March 1, 2026Ophthalmology ScienceOpen Access

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The study aims to evaluate the effectiveness of deep-reasoning large language models in diagnosing ophthalmic emergencies.
Cross-sectional study design using 34 emergency ophthalmology cases.
Cases reconstructed into three information levels: history, basic examination, and specialist examination.
Outputs from six LLMs were independently scored by two ophthalmologists for diagnostic correctness and triage accuracy.
46.9% of diagnoses were fully correct, increasing from 43.1% at L1 to 53.9% at L3.
Overall triage-category accuracy reached 85.3%.
Ancillary tests showed a majority of under-testing recommendations at 51%.

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Comparative evaluation measures diagnostic accuracy in ophthalmic emergencies, indicating the varying effectiveness of AI models.

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Lin et al. (2026) studied this question.

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