{"title":"训练数据告诉我们很多关于健康人工智能工具可能受益的信息。","authors":"Alison P Paprica","doi":"10.12927/hcpap.2025.27569","DOIUrl":null,"url":null,"abstract":"<p><p>Appropriate training data are a prerequisite for health AI tools. Policy makers, clinicians and patients can assess the datasets used to train AI models as a practical step in determining whom health AI tools are likely to benefit. Analyses of training datasets can help prioritize which health AI tools to validate and help identify where changes are needed to improve the equity of health AI.</p>","PeriodicalId":101342,"journal":{"name":"HealthcarePapers","volume":"22 4","pages":"58-62"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Training Data Tell Us a Lot About Whom Health AI Tools Are Likely to Benefit.\",\"authors\":\"Alison P Paprica\",\"doi\":\"10.12927/hcpap.2025.27569\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Appropriate training data are a prerequisite for health AI tools. Policy makers, clinicians and patients can assess the datasets used to train AI models as a practical step in determining whom health AI tools are likely to benefit. Analyses of training datasets can help prioritize which health AI tools to validate and help identify where changes are needed to improve the equity of health AI.</p>\",\"PeriodicalId\":101342,\"journal\":{\"name\":\"HealthcarePapers\",\"volume\":\"22 4\",\"pages\":\"58-62\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"HealthcarePapers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.12927/hcpap.2025.27569\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"HealthcarePapers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12927/hcpap.2025.27569","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Training Data Tell Us a Lot About Whom Health AI Tools Are Likely to Benefit.
Appropriate training data are a prerequisite for health AI tools. Policy makers, clinicians and patients can assess the datasets used to train AI models as a practical step in determining whom health AI tools are likely to benefit. Analyses of training datasets can help prioritize which health AI tools to validate and help identify where changes are needed to improve the equity of health AI.
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