Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review

Background and Aims: Low-income populations in the United States experience disproportionate exposure to adverse social conditions. This scoping review examined artificial intelligence (AI) and machine learning (ML) approaches used in United States health systems to identify financial hardship, housing instability, food insecurity, neighborhood deprivation, and other social determinants of health (SDOH), and characterized interventions implemented in response. Methods: Following the Arksey and O'Malley framework and PRISMA-ScR guidance, MEDLINE, CINAHL Plus with Full Text, PsycINFO, and Academic Search Ultimate were searched for peer-reviewed studies published from January 2010 through January 2026. The primary reviewer screened 209 unique records, and two additional reviewers reviewed screening decisions. Disagreements were resolved through discussion and majority vote. Seventeen studies met the inclusion criteria. Results: Studies were conducted in academic medical centers, integrated delivery networks, safety-net hospitals, Veterans Health Administration facilities, specialty clinics, and Medicaid administrative environments. Methods included predictive ML modeling, natural language processing, and unsupervised clustering, with electronic health records used in 16 of 17 studies. Only one study evaluated a structured intervention: a proactive financial assistance program for ambulatory oncology patients at risk of financial hardship and unmet social needs. The remaining studies focused primarily on risk identification or model development. External validation was uncommon, and several studies reported differential performance across socioeconomic or racial and ethnic groups. Conclusion: AI and ML may support SDOH risk identification in low-income populations, but evidence of generalizability, fairness, and translation into effective interventions remains limited. Future research should prioritize external validation, equity-focused evaluation, and rigorous testing of interventions triggered by identified social risks.