{"title":"人工智能能让科学数据更加公平吗？","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":"{\"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}","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}
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.
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