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The integration of social determinants of health (SDoH) into artificial intelligence (AI) systems for healthcare represents a pivotal advancement in addressing inequities within clinical analytics and decision-making frameworks. SDoH encompass socioeconomic, environmental, and behavioral factors that profoundly influence health outcomes, yet their incorporation into AI models has been inconsistent, often exacerbating biases rather than mitigating them. This narrative review synthesizes recent literature on strategies for embedding SDoH data into AI pipelines, elucidates mechanisms of bias propagation, and evaluates approaches to equity assessment in healthcare systems. Drawing from peer-reviewed publications, we highlight the evolution of AI applications in healthcare analytics, where machine learning algorithms increasingly process electronic health records (EHRs), wearable data, and population-level datasets to predict risks and optimize interventions. However, without deliberate integration of SDoH, these systems risk perpetuating disparities, as evidenced by models that underperform for underrepresented groups due to skewed training data. Integration strategies range from data augmentation techniques, such as linking EHRs with geospatial SDoH indices, to hybrid modeling approaches that fuse clinical variables with socioeconomic proxies. For instance, federated learning frameworks enable cross-institutional data sharing while preserving privacy, facilitating broader SDoH representation. Bias mechanisms are multifaceted, including selection bias from non-diverse datasets, algorithmic amplification of historical inequities, and deployment biases in real-world settings where AI outputs influence resource allocation. Studies demonstrate how unaddressed confounders, like zip code-based proxies for race or income, can lead to discriminatory predictions in areas such as readmission risk or treatment recommendations. Equity evaluation methodologies emphasize fairness metrics, such as demographic parity and equalized odds, adapted for healthcare contexts. Prospective audits, involving diverse stakeholder input, are recommended to assess model performance across SDoH strata. Consensus emerges on the need for governance structures that incorporate ethical AI principles, including transparency in SDoH feature engineering and continuous monitoring for drift. Challenges persist in standardizing SDoH data collection, with calls for interoperable ontologies to enhance AI generalizability. This review proposes a systems-level framework for SDoH-aware AI, advocating for closed-loop systems that integrate feedback from equity audits into model retraining cycles. Ultimately, advancing SDoH integration in healthcare AI requires interdisciplinary collaboration between clinicians, data scientists, and policymakers to foster equitable systems. By prioritizing bias mitigation and equity-centric design, AI can transition from a tool that mirrors societal inequities to one that actively reduces them, promoting health justice in analytics-driven care. Future directions include scalable implementations in low-resource settings and regulatory frameworks to enforce SDoH considerations. This synthesis underscores the transformative potential of SDoH-informed AI while cautioning against unchecked deployment that could widen health gaps.

During the COVID-19 pandemic, machine learning models developed in high-resource hospitals achieved strong performance in predicting outcomes such as mortality, ICU admission, and mechanical ventilation, but their accuracy often degrades when applied to low-resource settings due to differences in patient populations, disease severity, clinical practices, and documentation quality. Low-resource hospitals also face limited patient volumes, incomplete labeled data, and strict privacy regulations (e.g., HIPAA and GDPR), which prevent centralized data sharing and hinder independent model development, creating a barrier to equitable AI deployment. To address this, we propose a federated transfer learning framework that adapts prognostic models from high-resource to low-resource hospitals without exchanging patient-level data. The approach transfers only aggregate statistics (e.g., feature means, variances, class-conditional distributions, and correlations) via a secure lightweight protocol, enabling target hospitals to align feature distributions using domain adaptation techniques and fine-tune models on small local datasets. The framework includes source model training, statistical aggregation, secure transmission, and target-side adaptation modules, ensuring no raw patient data leaves any institution. By relying on aggregate statistics, the method preserves privacy while mitigating domain shift and maintaining clinical utility across diverse healthcare environments. This scalable and privacy-preserving framework supports broader deployment of COVID-19 predictive models and provides a generalizable strategy for other medical conditions with heterogeneous healthcare settings.