{"title":"在真实案例场景中使用基于大语言模型的人工智能对糖尿病患者进行糖尿病视网膜病变初步筛查的建议。","authors":"Nikhil Gopalakrishnan, Aishwarya Joshi, Jay Chhablani, Naresh Kumar Yadav, Nikitha Gurram Reddy, Padmaja Kumari Rani, Ram Snehith Pulipaka, Rohit Shetty, Shivani Sinha, Vishma Prabhu, Ramesh Venkatesh","doi":"10.1186/s40942-024-00533-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases.</p><p><strong>Methods: </strong>Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the \"majority clinician response\" (defined as the maximum number of identical responses provided by the clinicians) and \"majority AI-platform\" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned.</p><p><strong>Results: </strong>Clinicians, AI platforms, and the \"majority clinician response\" and \"majority AI response\" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately.</p><p><strong>Conclusion: </strong>Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.</p><p><strong>Clinical trial registration: </strong>Not applicable.</p>","PeriodicalId":14289,"journal":{"name":"International Journal of Retina and Vitreous","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10809735/pdf/","citationCount":"0","resultStr":"{\"title\":\"Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios.\",\"authors\":\"Nikhil Gopalakrishnan, Aishwarya Joshi, Jay Chhablani, Naresh Kumar Yadav, Nikitha Gurram Reddy, Padmaja Kumari Rani, Ram Snehith Pulipaka, Rohit Shetty, Shivani Sinha, Vishma Prabhu, Ramesh Venkatesh\",\"doi\":\"10.1186/s40942-024-00533-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases.</p><p><strong>Methods: </strong>Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the \\\"majority clinician response\\\" (defined as the maximum number of identical responses provided by the clinicians) and \\\"majority AI-platform\\\" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned.</p><p><strong>Results: </strong>Clinicians, AI platforms, and the \\\"majority clinician response\\\" and \\\"majority AI response\\\" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately.</p><p><strong>Conclusion: </strong>Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.</p><p><strong>Clinical trial registration: </strong>Not applicable.</p>\",\"PeriodicalId\":14289,\"journal\":{\"name\":\"International Journal of Retina and Vitreous\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10809735/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Retina and Vitreous\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s40942-024-00533-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Retina and Vitreous","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40942-024-00533-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios.
Purpose: To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases.
Methods: Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the "majority clinician response" (defined as the maximum number of identical responses provided by the clinicians) and "majority AI-platform" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned.
Results: Clinicians, AI platforms, and the "majority clinician response" and "majority AI response" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately.
Conclusion: Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.
期刊介绍：
International Journal of Retina and Vitreous focuses on the ophthalmic subspecialty of vitreoretinal disorders. The journal presents original articles on new approaches to diagnosis, outcomes of clinical trials, innovations in pharmacological therapy and surgical techniques, as well as basic science advances that impact clinical practice. Topical areas include, but are not limited to: -Imaging of the retina, choroid and vitreous -Innovations in optical coherence tomography (OCT) -Small-gauge vitrectomy, retinal detachment, chromovitrectomy -Electroretinography (ERG), microperimetry, other functional tests -Intraocular tumors -Retinal pharmacotherapy & drug delivery -Diabetic retinopathy & other vascular diseases -Age-related macular degeneration (AMD) & other macular entities
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