ArticleDigital journal of ophthalmology : DJO2026
Evaluation of artificial language-adjusted readability on cataract surgery queries.
Article in Digital journal of ophthalmology : DJO, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Purpose: To analyze accuracy and readability of answers to cataract surgery queries produced by artificial intelligence (AI) models and determine whether AI models can significantly improve readability. Methods: Google Gemini Advanced, ChatGPT 4.0, and Microsoft Copilot Pro were prompted to answer 25 questions about cataract surgery, followed by a request to re-answer questions at a 6th-grade level. Objective readability of answers were measured with five validated reading formulas and word count. Accuracy and readability of each answer were further graded by three ophthalmologists. Comparisons were performed between original and 6th-grade versions and among the three AI models. Results: After being prompted to answer at a 6th-grade reading level, Google Gemini Advanced and Microsoft Copilot Pro had lower average reading level than ChatGPT 4.0 (8.04 vs 8.19 vs 9.43 [ Conclusions: AI models can simplify reading level of responses to common cataract surgery queries while maintaining accuracy.
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