{"title":"通过可解释性推进医疗保健领域的伦理AI。","authors":"Yilin Ning, Mingxuan Liu, Nan Liu","doi":"10.1016/j.patter.2025.101290","DOIUrl":null,"url":null,"abstract":"<p><p>Interpretability is essential for building trust in health artificial intelligence (AI), but ensuring trustworthiness requires addressing broader ethical concerns, such as fairness, privacy, and reliability. This opinion article discusses the multilayered role of interpretability and transparency in addressing these concerns by highlighting their fundamental contribution to the responsible adoption and regulation of health AI.</p>","PeriodicalId":36242,"journal":{"name":"Patterns","volume":"6 6","pages":"101290"},"PeriodicalIF":6.7000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191714/pdf/","citationCount":"0","resultStr":"{\"title\":\"Advancing ethical AI in healthcare through interpretability.\",\"authors\":\"Yilin Ning, Mingxuan Liu, Nan Liu\",\"doi\":\"10.1016/j.patter.2025.101290\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Interpretability is essential for building trust in health artificial intelligence (AI), but ensuring trustworthiness requires addressing broader ethical concerns, such as fairness, privacy, and reliability. This opinion article discusses the multilayered role of interpretability and transparency in addressing these concerns by highlighting their fundamental contribution to the responsible adoption and regulation of health AI.</p>\",\"PeriodicalId\":36242,\"journal\":{\"name\":\"Patterns\",\"volume\":\"6 6\",\"pages\":\"101290\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12191714/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Patterns\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.patter.2025.101290\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Patterns","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.patter.2025.101290","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Advancing ethical AI in healthcare through interpretability.
Interpretability is essential for building trust in health artificial intelligence (AI), but ensuring trustworthiness requires addressing broader ethical concerns, such as fairness, privacy, and reliability. This opinion article discusses the multilayered role of interpretability and transparency in addressing these concerns by highlighting their fundamental contribution to the responsible adoption and regulation of health AI.