{"title":"Can AI make scientific data more equitable?","authors":"","doi":"10.1038/s44222-024-00263-5","DOIUrl":null,"url":null,"abstract":"Biased and unrepresentative scientific data can lead to misleading conclusions and potentially harm patients. Artificial intelligence (AI) might be able to help make data more representative, but only if a standardized approach to assessing the quality of AI-generated data is established.","PeriodicalId":74248,"journal":{"name":"Nature reviews bioengineering","volume":"2 12","pages":"981-981"},"PeriodicalIF":0.0000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s44222-024-00263-5.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature reviews bioengineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44222-024-00263-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Biased and unrepresentative scientific data can lead to misleading conclusions and potentially harm patients. Artificial intelligence (AI) might be able to help make data more representative, but only if a standardized approach to assessing the quality of AI-generated data is established.