{"title":"人工智能在职业健康领域的临床应用：系统性文献综述。","authors":"Zaira S Chaudhry, Avishek Choudhury","doi":"10.1097/JOM.0000000000003212","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>The aims of the study are to identify and to critically analyze studies using artificial intelligence (AI) in occupational health.</p><p><strong>Methods: </strong>A systematic search of PubMed, IEEE Xplore, and Web of Science was conducted to identify relevant articles published in English between January 2014-January 2024. Quality was assessed with the validated APPRAISE-AI tool.</p><p><strong>Results: </strong>The 27 included articles were categorized as follows: health risk assessment ( n = 17), return to work and disability duration ( n = 5), injury severity ( n = 3), and injury management ( n = 2). Forty-seven AI algorithms were utilized, with artificial neural networks, support vector machines, and random forest being most common. Model accuracy ranged from 0.60-0.99 and area under the curve (AUC) from 0.7-1.0. Most studies ( n = 15) were of moderate quality.</p><p><strong>Conclusions: </strong>While AI has potential clinical utility in occupational health, explainable models that are rigorously validated in real-world settings are warranted.</p>","PeriodicalId":94100,"journal":{"name":"Journal of occupational and environmental medicine","volume":" ","pages":"943-955"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical Applications of Artificial Intelligence in Occupational Health: A Systematic Literature Review.\",\"authors\":\"Zaira S Chaudhry, Avishek Choudhury\",\"doi\":\"10.1097/JOM.0000000000003212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>The aims of the study are to identify and to critically analyze studies using artificial intelligence (AI) in occupational health.</p><p><strong>Methods: </strong>A systematic search of PubMed, IEEE Xplore, and Web of Science was conducted to identify relevant articles published in English between January 2014-January 2024. Quality was assessed with the validated APPRAISE-AI tool.</p><p><strong>Results: </strong>The 27 included articles were categorized as follows: health risk assessment ( n = 17), return to work and disability duration ( n = 5), injury severity ( n = 3), and injury management ( n = 2). Forty-seven AI algorithms were utilized, with artificial neural networks, support vector machines, and random forest being most common. Model accuracy ranged from 0.60-0.99 and area under the curve (AUC) from 0.7-1.0. Most studies ( n = 15) were of moderate quality.</p><p><strong>Conclusions: </strong>While AI has potential clinical utility in occupational health, explainable models that are rigorously validated in real-world settings are warranted.</p>\",\"PeriodicalId\":94100,\"journal\":{\"name\":\"Journal of occupational and environmental medicine\",\"volume\":\" \",\"pages\":\"943-955\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of occupational and environmental medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1097/JOM.0000000000003212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of occupational and environmental medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1097/JOM.0000000000003212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/26 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Clinical Applications of Artificial Intelligence in Occupational Health: A Systematic Literature Review.
Objectives: The aims of the study are to identify and to critically analyze studies using artificial intelligence (AI) in occupational health.
Methods: A systematic search of PubMed, IEEE Xplore, and Web of Science was conducted to identify relevant articles published in English between January 2014-January 2024. Quality was assessed with the validated APPRAISE-AI tool.
Results: The 27 included articles were categorized as follows: health risk assessment ( n = 17), return to work and disability duration ( n = 5), injury severity ( n = 3), and injury management ( n = 2). Forty-seven AI algorithms were utilized, with artificial neural networks, support vector machines, and random forest being most common. Model accuracy ranged from 0.60-0.99 and area under the curve (AUC) from 0.7-1.0. Most studies ( n = 15) were of moderate quality.
Conclusions: While AI has potential clinical utility in occupational health, explainable models that are rigorously validated in real-world settings are warranted.
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