Large Language Models: Pioneering New Educational Frontiers in Childhood Myopia DOI Creative Commons
Mohammad Delsoz, Amr K. Hassan, Amin Nabavi

et al.

Ophthalmology and Therapy, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

This study aimed to evaluate the performance of three large language models (LLMs), namely ChatGPT-3.5, ChatGPT-4o (o1 Preview), and Google Gemini, in producing patient education materials (PEMs) improving readability online PEMs on childhood myopia. LLM-generated responses were assessed using prompts. Prompt A requested "Write educational material myopia." B added a modifier specifying "a sixth-grade reading level FKGL (Flesch-Kincaid Grade Level) formula." C rewrite existing FKGL. Reponses for quality (DISCERN tool), (FKGL, SMOG (Simple Measure Gobbledygook)), Patient Education Materials Assessment Tool (PEMAT, understandability/actionability), accuracy. (01) ChatGPT-3.5 generated good-quality 52.8 52.7, respectively); however, declined from prompt (p = 0.001 p 0.013). Gemini produced fair-quality 43) but improved with 0.02). All exceeded 70% PEMAT understandability threshold failed actionability (40%). No misinformation was identified. Readability B; achieved or below (FGKL 6 ± 0.6 6.2 0.3), while did not 7 0.6). outperformed < 0.001) comparable 0.846). across all LLMs, Preview) showing most significant gains (FKGL 5.8 1.5; 0.001). demonstrates potential accurate, good-quality, understandable PEMs,

Language: Английский

Large Language Models: Pioneering New Educational Frontiers in Childhood Myopia DOI Creative Commons
Mohammad Delsoz, Amr K. Hassan, Amin Nabavi

et al.

Ophthalmology and Therapy, Journal Year: 2025, Volume and Issue: unknown

Published: April 21, 2025

This study aimed to evaluate the performance of three large language models (LLMs), namely ChatGPT-3.5, ChatGPT-4o (o1 Preview), and Google Gemini, in producing patient education materials (PEMs) improving readability online PEMs on childhood myopia. LLM-generated responses were assessed using prompts. Prompt A requested "Write educational material myopia." B added a modifier specifying "a sixth-grade reading level FKGL (Flesch-Kincaid Grade Level) formula." C rewrite existing FKGL. Reponses for quality (DISCERN tool), (FKGL, SMOG (Simple Measure Gobbledygook)), Patient Education Materials Assessment Tool (PEMAT, understandability/actionability), accuracy. (01) ChatGPT-3.5 generated good-quality 52.8 52.7, respectively); however, declined from prompt (p = 0.001 p 0.013). Gemini produced fair-quality 43) but improved with 0.02). All exceeded 70% PEMAT understandability threshold failed actionability (40%). No misinformation was identified. Readability B; achieved or below (FGKL 6 ± 0.6 6.2 0.3), while did not 7 0.6). outperformed < 0.001) comparable 0.846). across all LLMs, Preview) showing most significant gains (FKGL 5.8 1.5; 0.001). demonstrates potential accurate, good-quality, understandable PEMs,

Language: Английский

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