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New research · Ophthalmology
International journal of retina and vitreous · 22h
AI / informaticsInternational journal of retina and vitreous · 2026

Large language model based simplification of ophthalmological clinical and ancillary results for patient readability.

Alexandre Antônio Marques Rosa, Francisco Vinícius Moraes de Souza, José Leandro Nascimento da Silva … Taurino Dos Santos Rodrigues Neto
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OphthalmologyAI / informatics

Large language models accurately simplify ophthalmological reports for patients.

Large language model based simplification of ophthalmological clinical and ancillary results for patient readability.

Alexandre Antônio Marques Rosa et al. · International journal of retina and vitreous · 2026
Background

Ophthalmological reports are often written at a complexity level that exceeds the reading ability of many patients.

Purpose

This study evaluated whether different prompting strategies improve the readability and safety of simplified ophthalmological reports.

Methods

We analyzed 443 de-identified reports from a tele-ophthalmology platform, including 280 retinal fundus and 163 ocular ultrasound reports.

91%
Results
simplified reports were factually correct
More results

The original reports were highly complex, with a median aRGL of 13.8 overall, 11.6 for fundus reports, and 15.4 for ocular ultrasound reports.

Prompt engineering was a major determinant of performance, and the targeted 7th-grade prompt produced the best results across models.

More results

Copilot, Gemini, and GPT-4.0 achieved median aRGL values closest to the recommended patient-facing reading level, while GPT-3.5 showed weaker performance in some comparisons.

Clinical validation showed substantial agreement between raters (kappa range, 0.70-0.97).

“
Conclusion

LLMs can simplify ophthalmological reports while preserving clinical fidelity, but performance depends strongly on prompt specificity. These tools show promise for patient-facing communication, although human oversight remains essential.

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