{"title":"人工智能在干眼症临床诊断和治疗中的应用：工作流程、有效性和评估。","authors":"Mingzhi Lu, Kuiliang Yang, Xiaoxi Deng, Tingting Fan, Han Zhang, Wanju Yang, Yiqiao Xing","doi":"10.4103/joco.joco_172_24","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To introduce the applications of artificial intelligence (AI) in the clinical diagnosis and treatment of dry eye (DE) and to explore its common workflows, effectiveness, challenges, and future development directions.</p><p><strong>Methods: </strong>This article conducts a literature review, focusing on the applications of AI in the diagnosis and treatment of DE. The primary search terms include \"artificial intelligence\", \"machine learning\", \"deep learning\", \"computer-aided\", and \"Dry Eye\".</p><p><strong>Results: </strong>A total of 48 relevant original studies were identified, and their algorithms, sample characteristics, and data types were summarized. Through data analysis and image recognition, AI assists in DE examinations, identifies risk factors, aids diagnosis, and manages and monitors treatment. AI excels in enhancing diagnostic efficiency, accuracy, and objectivity, and shows promise in cloud-based treatment management. However, the applications of AI in DE also face certain challenges that need to be addressed.</p><p><strong>Conclusions: </strong>AI has the potential to revolutionize the diagnosis of DE and recommend personalized treatment strategies. This review summarizes existing challenges and offers clinicians and researchers a comprehensive, objective overview of AI applications and workflows in DE.</p>","PeriodicalId":15423,"journal":{"name":"Journal of Current Ophthalmology","volume":"36 4","pages":"315-324"},"PeriodicalIF":0.9000,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487795/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.\",\"authors\":\"Mingzhi Lu, Kuiliang Yang, Xiaoxi Deng, Tingting Fan, Han Zhang, Wanju Yang, Yiqiao Xing\",\"doi\":\"10.4103/joco.joco_172_24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To introduce the applications of artificial intelligence (AI) in the clinical diagnosis and treatment of dry eye (DE) and to explore its common workflows, effectiveness, challenges, and future development directions.</p><p><strong>Methods: </strong>This article conducts a literature review, focusing on the applications of AI in the diagnosis and treatment of DE. The primary search terms include \\\"artificial intelligence\\\", \\\"machine learning\\\", \\\"deep learning\\\", \\\"computer-aided\\\", and \\\"Dry Eye\\\".</p><p><strong>Results: </strong>A total of 48 relevant original studies were identified, and their algorithms, sample characteristics, and data types were summarized. Through data analysis and image recognition, AI assists in DE examinations, identifies risk factors, aids diagnosis, and manages and monitors treatment. AI excels in enhancing diagnostic efficiency, accuracy, and objectivity, and shows promise in cloud-based treatment management. However, the applications of AI in DE also face certain challenges that need to be addressed.</p><p><strong>Conclusions: </strong>AI has the potential to revolutionize the diagnosis of DE and recommend personalized treatment strategies. This review summarizes existing challenges and offers clinicians and researchers a comprehensive, objective overview of AI applications and workflows in DE.</p>\",\"PeriodicalId\":15423,\"journal\":{\"name\":\"Journal of Current Ophthalmology\",\"volume\":\"36 4\",\"pages\":\"315-324\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2025-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12487795/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Current Ophthalmology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4103/joco.joco_172_24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Current Ophthalmology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4103/joco.joco_172_24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
Artificial Intelligence in Clinical Diagnosis and Treatment of Dry Eye: Workflows, Effectiveness, and Evaluation.
Purpose: To introduce the applications of artificial intelligence (AI) in the clinical diagnosis and treatment of dry eye (DE) and to explore its common workflows, effectiveness, challenges, and future development directions.
Methods: This article conducts a literature review, focusing on the applications of AI in the diagnosis and treatment of DE. The primary search terms include "artificial intelligence", "machine learning", "deep learning", "computer-aided", and "Dry Eye".
Results: A total of 48 relevant original studies were identified, and their algorithms, sample characteristics, and data types were summarized. Through data analysis and image recognition, AI assists in DE examinations, identifies risk factors, aids diagnosis, and manages and monitors treatment. AI excels in enhancing diagnostic efficiency, accuracy, and objectivity, and shows promise in cloud-based treatment management. However, the applications of AI in DE also face certain challenges that need to be addressed.
Conclusions: AI has the potential to revolutionize the diagnosis of DE and recommend personalized treatment strategies. This review summarizes existing challenges and offers clinicians and researchers a comprehensive, objective overview of AI applications and workflows in DE.
期刊介绍：
Peer Review under the responsibility of Iranian Society of Ophthalmology Journal of Current Ophthalmology, the official publication of the Iranian Society of Ophthalmology, is a peer-reviewed, open-access, scientific journal that welcomes high quality original articles related to vision science and all fields of ophthalmology. Journal of Current Ophthalmology is the continuum of Iranian Journal of Ophthalmology published since 1969.
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