{"title":"The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities - a scoping review.","authors":"Syed Ali Hussain, Mary Bresnahan, Jie Zhuang","doi":"10.1080/13557858.2024.2422848","DOIUrl":null,"url":null,"abstract":"<p><p>This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and the consequences of AIHA for health equity. Using the PRISMA-ScR guidelines, databases were searched between 2020 and 2024 using the terms racial and ethnic bias in health algorithms resulting in a final sample of 23 sources. Suggestions for how to mitigate algorithmic bias were compiled and evaluated, roles played by stakeholders were identified, and governance and stewardship plans for AIHA were examined. While AIHA represent a significant breakthrough in predictive analytics and treatment optimization, regularly outperforming humans in diagnostic precision and accuracy, they also present serious challenges to patient privacy, data security, institutional transparency, and health equity. Evidence from extant sources including those in this review showed that AIHA carry the potential to perpetuate health inequities. While the current study considered AIHA in the US, the use of AIHA carries implications for global health equity.</p>","PeriodicalId":51038,"journal":{"name":"Ethnicity & Health","volume":" ","pages":"1-18"},"PeriodicalIF":2.6000,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ethnicity & Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/13557858.2024.2422848","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ETHNIC STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and the consequences of AIHA for health equity. Using the PRISMA-ScR guidelines, databases were searched between 2020 and 2024 using the terms racial and ethnic bias in health algorithms resulting in a final sample of 23 sources. Suggestions for how to mitigate algorithmic bias were compiled and evaluated, roles played by stakeholders were identified, and governance and stewardship plans for AIHA were examined. While AIHA represent a significant breakthrough in predictive analytics and treatment optimization, regularly outperforming humans in diagnostic precision and accuracy, they also present serious challenges to patient privacy, data security, institutional transparency, and health equity. Evidence from extant sources including those in this review showed that AIHA carry the potential to perpetuate health inequities. While the current study considered AIHA in the US, the use of AIHA carries implications for global health equity.
期刊介绍：
Ethnicity & Health
is an international academic journal designed to meet the world-wide interest in the health of ethnic groups. It embraces original papers from the full range of disciplines concerned with investigating the relationship between ’ethnicity’ and ’health’ (including medicine and nursing, public health, epidemiology, social sciences, population sciences, and statistics). The journal also covers issues of culture, religion, gender, class, migration, lifestyle and racism, in so far as they relate to health and its anthropological and social aspects.
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