Rayna F Marshall, Krishna Mallem, Hannah Xu, Jennifer Thorne, Bryn Burkholder, Benjamin Chaon, Paulina Liberman, Meghan Berkenstock
{"title":"调查关于葡萄膜炎的人工智能大型语言模型的准确性和完整性:对 ChatGPT 的评估。","authors":"Rayna F Marshall, Krishna Mallem, Hannah Xu, Jennifer Thorne, Bryn Burkholder, Benjamin Chaon, Paulina Liberman, Meghan Berkenstock","doi":"10.1080/09273948.2024.2317417","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To assess the accuracy and completeness of ChatGPT-generated answers regarding uveitis description, prevention, treatment, and prognosis.</p><p><strong>Methods: </strong>Thirty-two uveitis-related questions were generated by a uveitis specialist and inputted into ChatGPT 3.5. Answers were compiled into a survey and were reviewed by five uveitis specialists using standardized Likert scales of accuracy and completeness.</p><p><strong>Results: </strong>In total, the median accuracy score for all the uveitis questions (<i>n</i> = 32) was 4.00 (between \"more correct than incorrect\" and \"nearly all correct\"), and the median completeness score was 2.00 (\"adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete\"). The interrater variability assessment had a total kappa value of 0.0278 for accuracy and 0.0847 for completeness.</p><p><strong>Conclusion: </strong>ChatGPT can provide relatively high accuracy responses for various questions related to uveitis; however, the answers it provides are incomplete, with some inaccuracies. Its utility in providing medical information requires further validation and development prior to serving as a source of uveitis information for patients.</p>","PeriodicalId":19406,"journal":{"name":"Ocular Immunology and Inflammation","volume":" ","pages":"2052-2055"},"PeriodicalIF":2.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the Accuracy and Completeness of an Artificial Intelligence Large Language Model About Uveitis: An Evaluation of ChatGPT.\",\"authors\":\"Rayna F Marshall, Krishna Mallem, Hannah Xu, Jennifer Thorne, Bryn Burkholder, Benjamin Chaon, Paulina Liberman, Meghan Berkenstock\",\"doi\":\"10.1080/09273948.2024.2317417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To assess the accuracy and completeness of ChatGPT-generated answers regarding uveitis description, prevention, treatment, and prognosis.</p><p><strong>Methods: </strong>Thirty-two uveitis-related questions were generated by a uveitis specialist and inputted into ChatGPT 3.5. Answers were compiled into a survey and were reviewed by five uveitis specialists using standardized Likert scales of accuracy and completeness.</p><p><strong>Results: </strong>In total, the median accuracy score for all the uveitis questions (<i>n</i> = 32) was 4.00 (between \\\"more correct than incorrect\\\" and \\\"nearly all correct\\\"), and the median completeness score was 2.00 (\\\"adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete\\\"). The interrater variability assessment had a total kappa value of 0.0278 for accuracy and 0.0847 for completeness.</p><p><strong>Conclusion: </strong>ChatGPT can provide relatively high accuracy responses for various questions related to uveitis; however, the answers it provides are incomplete, with some inaccuracies. Its utility in providing medical information requires further validation and development prior to serving as a source of uveitis information for patients.</p>\",\"PeriodicalId\":19406,\"journal\":{\"name\":\"Ocular Immunology and Inflammation\",\"volume\":\" \",\"pages\":\"2052-2055\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ocular Immunology and Inflammation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/09273948.2024.2317417\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/2/23 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ocular Immunology and Inflammation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/09273948.2024.2317417","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/2/23 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Investigating the Accuracy and Completeness of an Artificial Intelligence Large Language Model About Uveitis: An Evaluation of ChatGPT.
Purpose: To assess the accuracy and completeness of ChatGPT-generated answers regarding uveitis description, prevention, treatment, and prognosis.
Methods: Thirty-two uveitis-related questions were generated by a uveitis specialist and inputted into ChatGPT 3.5. Answers were compiled into a survey and were reviewed by five uveitis specialists using standardized Likert scales of accuracy and completeness.
Results: In total, the median accuracy score for all the uveitis questions (n = 32) was 4.00 (between "more correct than incorrect" and "nearly all correct"), and the median completeness score was 2.00 ("adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete"). The interrater variability assessment had a total kappa value of 0.0278 for accuracy and 0.0847 for completeness.
Conclusion: ChatGPT can provide relatively high accuracy responses for various questions related to uveitis; however, the answers it provides are incomplete, with some inaccuracies. Its utility in providing medical information requires further validation and development prior to serving as a source of uveitis information for patients.
期刊介绍:
Ocular Immunology & Inflammation ranks 18 out of 59 in the Ophthalmology Category.Ocular Immunology and Inflammation is a peer-reviewed, scientific publication that welcomes the submission of original, previously unpublished manuscripts directed to ophthalmologists and vision scientists. Published bimonthly, the journal provides an international medium for basic and clinical research reports on the ocular inflammatory response and its control by the immune system. The journal publishes original research papers, case reports, reviews, letters to the editor, meeting abstracts, and invited editorials.