A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments - David Sunday Araoti (Emerging Trends in Metabolites, 2026)
The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial intelligence (AI) to support predictive health systems in resource-constrained environments. Metabolomics enables comprehensive profiling of small-molecule metabolites, providing dynamic insights into physiological and pathological processes. When combined with machine learning techniques, these datasets can be analyzed to identify early biomarkers, assess disease risk, and inform personalized intervention strategies. The proposed framework outlines a multi-layered system architecture comprising metabolite data acquisition, preprocessing, feature extraction, and AI-based predictive modeling. The system is designed to generate clinically relevant outputs, including early diagnostic indicators, risk classification, and decision- support insights for diseases such as diabetes, cardiovascular conditions, and cancer. Emphasis is placed on scalability through the integration of portable diagnostic tools, cloud-based data processing, and decentralized healthcare delivery models. The study also examines key ethical and governance considerations, including data privacy, algorithmic fairness, and equitable access to digital health technologies. By aligning technological innovation with the realities of low-resource settings, the framework aims to enhance early disease detection and improve healthcare outcomes. Overall, this research contributes to the advancement of predictive and precision public health by proposing a scalable, interdisciplinary model that bridges biological data analysis and computational intelligence for improved health system performance in underserved populations.
Keywords: Biomarkers; Precision medicine; Predictive Healthcare; Resource-Constrained Environments; artificial intelligence; digital health; metabolomics