{"title":"Reducing bias in healthcare artificial intelligence: A white paper.","authors":"Carolyn Sun, Shannon L Harris","doi":"10.1177/14604582241291410","DOIUrl":null,"url":null,"abstract":"<p><p><b>Objective:</b> Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. <b>Methods:</b> At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. <b>Results:</b> This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. <b>Conclusions:</b> Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Informatics Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/14604582241291410","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.
期刊介绍：
Health Informatics Journal is an international peer-reviewed journal. All papers submitted to Health Informatics Journal are subject to peer review by members of a carefully appointed editorial board. The journal operates a conventional single-blind reviewing policy in which the reviewer’s name is always concealed from the submitting author.
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