ChatGPT: is it good for our glaucoma patients?

Author:

Wu Gloria,Lee David A.,Zhao Weichen,Wong Adrial,Sidhu Sahej

Abstract

PurposeOur study investigates ChatGPT and its ability to communicate with glaucoma patients.MethodsWe inputted eight glaucoma-related questions/topics found on the American Academy of Ophthalmology (AAO)’s website into ChatGPT. We used the Flesch–Kincaid test, Gunning Fog Index, SMOG Index, and Dale–Chall readability formula to evaluate the comprehensibility of its responses for patients. ChatGPT’s answers were compared with those found on the AAO’s website.ResultsChatGPT’s responses required reading comprehension of a higher grade level (average = grade 12.5 ± 1.6) than that of the text on the AAO’s website (average = 9.4 grade ± 3.5), (0.0384). For the eight responses, the key ophthalmic terms appeared 34 out of 86 times in the ChatGPT responses vs. 86 out of 86 times in the text on the AAO’s website. The term “eye doctor” appeared once in the ChatGPT text, but the formal term “ophthalmologist” did not appear. The term “ophthalmologist” appears 26 times on the AAO’s website. The word counts of the answers produced by ChatGPT and those on the AAO’s website were similar (p = 0.571), with phrases of a homogenous length.ConclusionChatGPT trains on the texts, phrases, and algorithms inputted by software engineers. As ophthalmologists, through our websites and journals, we should consider encoding the phrase “see an ophthalmologist”. Our medical assistants should sit with patients during their appointments to ensure that the text is accurate and that they fully comprehend its meaning. ChatGPT is effective for providing general information such as definitions or potential treatment options for glaucoma. However, ChatGPT has a tendency toward repetitive answers and, due to their elevated readability scores, these could be too difficult for a patient to read.

Publisher

Frontiers Media SA

Reference27 articles.

1. The role of chatGPT, generative language models, and artificial intelligence in medical education: A conversation with chatGPT and a call for papers;Eysenbach;JMIR Med Educ,2023

2. KutnerM GreenbergE JinY Literacy in Everyday Life: Results from the 2003 National Assessment of Adult Literacy [U.S. Department of Education web site]2007

3. CamilleLR BaumanK Educational Attainment in the United States: 2015. [U.S. Census Bureau web site]2016

4. Relationship Between Coronavirus-Related eHealth Literacy and COVID-19 Knowledge, Attitudes, and Practices among US Adults: Web-Based Survey Study;An;J Med Internet Res,2021

5. Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 chatbot for clinical vignettes with common chief complaints: A pilot study;Hirosawa;Int J Environ Res Public Health,2023

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3