Syed Ali Haider, Sahar Borna, Cesar A Gomez-Cabello, Sophia M Pressman, Clifton R Haider, Antonio Jorge Forte
{"title":"算法鸿沟：对医疗保健中人工智能驱动的种族差异的系统回顾。","authors":"Syed Ali Haider, Sahar Borna, Cesar A Gomez-Cabello, Sophia M Pressman, Clifton R Haider, Antonio Jorge Forte","doi":"10.1007/s40615-024-02237-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfaced. AI's effectiveness correlates not only with the algorithm's quality but also with its training data's integrity. This systematic review investigates the racial disparities perpetuated by AI systems across diverse medical domains and the implications of deploying them, particularly in healthcare.</p><p><strong>Methods: </strong>Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer-reviewed articles in English from 2013 to 2023 that examined instances of racial bias perpetuated by AI in healthcare. Studies conducted outside of healthcare settings or that addressed biases other than racial, as well as letters, opinions were excluded. The risk of bias was identified using CASP criteria for reviews and the Modified Newcastle Scale for observational studies.</p><p><strong>Results: </strong>Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1272 articles were initially identified, from which 26 met eligibility criteria. Four articles were identified via snowballing, resulting in 30 articles in the analysis. Studies indicate a significant association between AI utilization and the exacerbation of racial disparities, especially in minority populations, including Blacks and Hispanics. Biased data, algorithm design, unfair deployment of algorithms, and historic/systemic inequities were identified as the causes. Study limitations stem from heterogeneity impeding broad comparisons and the preclusion of meta-analysis.</p><p><strong>Conclusion: </strong>To address racial disparities in healthcare outcomes, enhanced ethical considerations and regulatory frameworks are needed in AI healthcare applications. Comprehensive bias detection tools and mitigation strategies, coupled with active supervision by physicians, are essential to ensure AI becomes a tool for reducing racial disparities in healthcare outcomes.</p>","PeriodicalId":16921,"journal":{"name":"Journal of Racial and Ethnic Health Disparities","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.\",\"authors\":\"Syed Ali Haider, Sahar Borna, Cesar A Gomez-Cabello, Sophia M Pressman, Clifton R Haider, Antonio Jorge Forte\",\"doi\":\"10.1007/s40615-024-02237-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfaced. AI's effectiveness correlates not only with the algorithm's quality but also with its training data's integrity. This systematic review investigates the racial disparities perpetuated by AI systems across diverse medical domains and the implications of deploying them, particularly in healthcare.</p><p><strong>Methods: </strong>Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer-reviewed articles in English from 2013 to 2023 that examined instances of racial bias perpetuated by AI in healthcare. Studies conducted outside of healthcare settings or that addressed biases other than racial, as well as letters, opinions were excluded. The risk of bias was identified using CASP criteria for reviews and the Modified Newcastle Scale for observational studies.</p><p><strong>Results: </strong>Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1272 articles were initially identified, from which 26 met eligibility criteria. Four articles were identified via snowballing, resulting in 30 articles in the analysis. Studies indicate a significant association between AI utilization and the exacerbation of racial disparities, especially in minority populations, including Blacks and Hispanics. Biased data, algorithm design, unfair deployment of algorithms, and historic/systemic inequities were identified as the causes. Study limitations stem from heterogeneity impeding broad comparisons and the preclusion of meta-analysis.</p><p><strong>Conclusion: </strong>To address racial disparities in healthcare outcomes, enhanced ethical considerations and regulatory frameworks are needed in AI healthcare applications. Comprehensive bias detection tools and mitigation strategies, coupled with active supervision by physicians, are essential to ensure AI becomes a tool for reducing racial disparities in healthcare outcomes.</p>\",\"PeriodicalId\":16921,\"journal\":{\"name\":\"Journal of Racial and Ethnic Health Disparities\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Racial and Ethnic Health Disparities\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s40615-024-02237-0\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Racial and Ethnic Health Disparities","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s40615-024-02237-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.
Introduction: As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfaced. AI's effectiveness correlates not only with the algorithm's quality but also with its training data's integrity. This systematic review investigates the racial disparities perpetuated by AI systems across diverse medical domains and the implications of deploying them, particularly in healthcare.
Methods: Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer-reviewed articles in English from 2013 to 2023 that examined instances of racial bias perpetuated by AI in healthcare. Studies conducted outside of healthcare settings or that addressed biases other than racial, as well as letters, opinions were excluded. The risk of bias was identified using CASP criteria for reviews and the Modified Newcastle Scale for observational studies.
Results: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1272 articles were initially identified, from which 26 met eligibility criteria. Four articles were identified via snowballing, resulting in 30 articles in the analysis. Studies indicate a significant association between AI utilization and the exacerbation of racial disparities, especially in minority populations, including Blacks and Hispanics. Biased data, algorithm design, unfair deployment of algorithms, and historic/systemic inequities were identified as the causes. Study limitations stem from heterogeneity impeding broad comparisons and the preclusion of meta-analysis.
Conclusion: To address racial disparities in healthcare outcomes, enhanced ethical considerations and regulatory frameworks are needed in AI healthcare applications. Comprehensive bias detection tools and mitigation strategies, coupled with active supervision by physicians, are essential to ensure AI becomes a tool for reducing racial disparities in healthcare outcomes.
期刊介绍：
Journal of Racial and Ethnic Health Disparities reports on the scholarly progress of work to understand, address, and ultimately eliminate health disparities based on race and ethnicity. Efforts to explore underlying causes of health disparities and to describe interventions that have been undertaken to address racial and ethnic health disparities are featured. Promising studies that are ongoing or studies that have longer term data are welcome, as are studies that serve as lessons for best practices in eliminating health disparities. Original research, systematic reviews, and commentaries presenting the state-of-the-art thinking on problems centered on health disparities will be considered for publication. We particularly encourage review articles that generate innovative and testable ideas, and constructive discussions and/or critiques of health disparities.Because the Journal of Racial and Ethnic Health Disparities receives a large number of submissions, about 30% of submissions to the Journal are sent out for full peer review.
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