Qais A Dihan, Andrew D Brown, Ana T Zaldivar, Kendall E Montgomery, Muhammad Z Chauhan, Seif E Abdelnaem, Arsalan A Ali, Sayena Jabbehdari, Amr Azzam, Ahmed B Sallam, Abdelrahman M Elhusseiny
{"title":"运用生成式人工智能加强早产儿视网膜病变患者教育。","authors":"Qais A Dihan, Andrew D Brown, Ana T Zaldivar, Kendall E Montgomery, Muhammad Z Chauhan, Seif E Abdelnaem, Arsalan A Ali, Sayena Jabbehdari, Amr Azzam, Ahmed B Sallam, Abdelrahman M Elhusseiny","doi":"10.3928/01913913-20250515-01","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the efficacy of large language models (LLMs) in generating patient education materials (PEMs) on retinopathy of prematurity (ROP).</p><p><strong>Methods: </strong>ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Gemini (Google AI) were compared on three separate prompts. Prompt A requested that each LLM generate a novel PEM on ROP. Prompt B requested generated PEMs at the 6th-grade reading level using the validated Simple Measure of Gobbledygook (SMOG) readability formula. Prompt C requested LLMs improve the readability of existing, human-written PEMs to a 6th-grade reading level. PEMs inserted into Prompt C were sourced through a Google search of \"retinopathy of prematurity.\" Each PEM was analyzed for readability (SMOG, Flesch-Kincaid Grade Level [FKGL]), quality (Patient Education Materials Assessment Tool [PEMAT], DISCERN), and accuracy (Likert Misinformation Scale).</p><p><strong>Results: </strong>LLM-generated PEMs were of high quality (median DISCERN = 4), understandable (PEMAT-U ≥ 70%), and accurate (Likert = 1). Prompt B generated more readable PEMs than Prompt A (<i>P</i> < .001). ChatGPT-4 and Gemini rewrote PEMs (Prompt C) from a baseline readability level (FKGL: 8.8 ± 1.9, SMOG: 8.6 ± 1.5) to the targeted 6th-grade reading level. Only ChatGPT-4 rewrites maintained high quality and reliability (median DISCERN = 4).</p><p><strong>Conclusions: </strong>LLMs, particularly ChatGPT-4, can serve as strong supplementary tools to automate the process of generating readable and high-quality PEMs for parents on ROP. <b>[<i>J Pediatr Ophthalmol Strabismus</i>. 20XX;X(X):XXX-XXX.]</b>.</p>","PeriodicalId":50095,"journal":{"name":"Journal of Pediatric Ophthalmology & Strabismus","volume":" ","pages":"1-10"},"PeriodicalIF":1.0000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementing Generative AI to Enhance Patient Education on Retinopathy of Prematurity.\",\"authors\":\"Qais A Dihan, Andrew D Brown, Ana T Zaldivar, Kendall E Montgomery, Muhammad Z Chauhan, Seif E Abdelnaem, Arsalan A Ali, Sayena Jabbehdari, Amr Azzam, Ahmed B Sallam, Abdelrahman M Elhusseiny\",\"doi\":\"10.3928/01913913-20250515-01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To evaluate the efficacy of large language models (LLMs) in generating patient education materials (PEMs) on retinopathy of prematurity (ROP).</p><p><strong>Methods: </strong>ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Gemini (Google AI) were compared on three separate prompts. Prompt A requested that each LLM generate a novel PEM on ROP. Prompt B requested generated PEMs at the 6th-grade reading level using the validated Simple Measure of Gobbledygook (SMOG) readability formula. Prompt C requested LLMs improve the readability of existing, human-written PEMs to a 6th-grade reading level. PEMs inserted into Prompt C were sourced through a Google search of \\\"retinopathy of prematurity.\\\" Each PEM was analyzed for readability (SMOG, Flesch-Kincaid Grade Level [FKGL]), quality (Patient Education Materials Assessment Tool [PEMAT], DISCERN), and accuracy (Likert Misinformation Scale).</p><p><strong>Results: </strong>LLM-generated PEMs were of high quality (median DISCERN = 4), understandable (PEMAT-U ≥ 70%), and accurate (Likert = 1). Prompt B generated more readable PEMs than Prompt A (<i>P</i> < .001). ChatGPT-4 and Gemini rewrote PEMs (Prompt C) from a baseline readability level (FKGL: 8.8 ± 1.9, SMOG: 8.6 ± 1.5) to the targeted 6th-grade reading level. 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Implementing Generative AI to Enhance Patient Education on Retinopathy of Prematurity.
Purpose: To evaluate the efficacy of large language models (LLMs) in generating patient education materials (PEMs) on retinopathy of prematurity (ROP).
Methods: ChatGPT-3.5 (OpenAI), ChatGPT-4 (OpenAI), and Gemini (Google AI) were compared on three separate prompts. Prompt A requested that each LLM generate a novel PEM on ROP. Prompt B requested generated PEMs at the 6th-grade reading level using the validated Simple Measure of Gobbledygook (SMOG) readability formula. Prompt C requested LLMs improve the readability of existing, human-written PEMs to a 6th-grade reading level. PEMs inserted into Prompt C were sourced through a Google search of "retinopathy of prematurity." Each PEM was analyzed for readability (SMOG, Flesch-Kincaid Grade Level [FKGL]), quality (Patient Education Materials Assessment Tool [PEMAT], DISCERN), and accuracy (Likert Misinformation Scale).
Results: LLM-generated PEMs were of high quality (median DISCERN = 4), understandable (PEMAT-U ≥ 70%), and accurate (Likert = 1). Prompt B generated more readable PEMs than Prompt A (P < .001). ChatGPT-4 and Gemini rewrote PEMs (Prompt C) from a baseline readability level (FKGL: 8.8 ± 1.9, SMOG: 8.6 ± 1.5) to the targeted 6th-grade reading level. Only ChatGPT-4 rewrites maintained high quality and reliability (median DISCERN = 4).
Conclusions: LLMs, particularly ChatGPT-4, can serve as strong supplementary tools to automate the process of generating readable and high-quality PEMs for parents on ROP. [J Pediatr Ophthalmol Strabismus. 20XX;X(X):XXX-XXX.].
期刊介绍：
The Journal of Pediatric Ophthalmology & Strabismus is a bimonthly peer-reviewed publication for pediatric ophthalmologists. The Journal has published original articles on the diagnosis, treatment, and prevention of eye disorders in the pediatric age group and the treatment of strabismus in all age groups for over 50 years.
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