Algorithmic Inequity in Aging: How AI-Driven Healthcare Decision Tools Amplify Socioeconomic Disparities in Health Outcomes Among Older Adults

Background and ObjectivesArtificial intelligence (AI) is increasingly used in healthcare decision-making to improve clinical efficiency and patient risk assessment. However, emerging evidence suggests these tools may disadvantage older adults from lower socioeconomic backgrounds, potentially worsening health disparities. This scoping review examines how AI-driven healthcare decision tools affect health outcomes among socioeconomically disadvantaged older adults.MethodsFollowing the Arksey and O'Malley framework and the PRISMA-ScR guidelines, I searched PubMed, CINAHL, Embase, Scopus, and Web of Science for peer-reviewed studies published between 2000 and 2025. Studies were screened using a Population, Concept, and Context (PCC) framework.ResultsThirty-three studies met inclusion criteria, revealing four themes: algorithmic bias in training data, socioeconomic and digital access barriers, differential outcomes by income and race, and governance gaps in AI deployment.DiscussionCurrent AI healthcare tools risk reinforcing socioeconomic health disparities among older adults, highlighting the need for equitable design, policy oversight, and regulatory reforms.