{"title":"朝着公平的人工智能驱动的医学文本生成。","authors":"Yumeng Zhang, Jiangning Song","doi":"10.1038/s43588-025-00807-8","DOIUrl":null,"url":null,"abstract":"A recent study assesses bias in artificial intelligence (AI)-generated medical language to find differences in age, sex, and ethnicity. An optimization technique is proposed to improve fairness without sacrificing performance.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 5","pages":"361-362"},"PeriodicalIF":18.3000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward fair AI-driven medical text generation\",\"authors\":\"Yumeng Zhang, Jiangning Song\",\"doi\":\"10.1038/s43588-025-00807-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recent study assesses bias in artificial intelligence (AI)-generated medical language to find differences in age, sex, and ethnicity. An optimization technique is proposed to improve fairness without sacrificing performance.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"5 5\",\"pages\":\"361-362\"},\"PeriodicalIF\":18.3000,\"publicationDate\":\"2025-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-025-00807-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-025-00807-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A recent study assesses bias in artificial intelligence (AI)-generated medical language to find differences in age, sex, and ethnicity. An optimization technique is proposed to improve fairness without sacrificing performance.
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