AI-ENHANCED MOLECULAR BIOMARKER IDENTIFICATION FOR SOCIALLY-LINKED HEALTH DISPARITIES IN CHRONIC INFLAMMATORY DISEASES
DOI:
https://doi.org/10.71000/vkhj5n93Keywords:
Artificial Intelligence; Health Disparities; Social Determinants of Health; Biomarkers; Chronic Inflammatory Diseases; Precision Medicine.Abstract
Background: Chronic inflammatory diseases (CIDs) impose a significant global burden, with outcomes starkly shaped by socioeconomic and behavioral factors, leading to pervasive health disparities. Understanding the biological embedding of these social determinants is crucial for advancing equitable precision medicine. This narrative review explores the emerging role of artificial intelligence (AI) in deciphering the complex interplay between social adversity and disease pathophysiology through molecular biomarker discovery.
Objective: This review aims to synthesize and critically evaluate recent advancements in AI-driven methodologies for identifying molecular biomarkers that link social determinants of health to disparities in CIDs.
Main Discussion Points: The analysis centers on several interconnected themes: the necessity of AI for integrating high-dimensional social and multi-omics data, the prominent biomarker classes identified (including epigenetic, transcriptomic, and metabolomic signatures), and the translation of population-level findings towards clinical risk stratification. Critical examination reveals consistent methodological limitations, such as predominant cross-sectional designs, risks of algorithmic bias, and challenges in establishing causality and generalizability.
Conclusion: AI is a powerful, transformative tool for uncovering the biosocial pathways of health inequity, consistently pointing to immune and stress-response systems as key mediators. However, the field is in its early stages, requiring more rigorous longitudinal and interventional study designs, a steadfast commitment to ethical and equitable AI development, and the integration of social biomarkers into holistic clinical frameworks to move from documenting disparities to actively addressing them.
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Copyright (c) 2025 Irfan, Javeria Naz, Shaikh Khalid Muhammad, Bisma Liaqat, Fahad Asim (Author)
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