What question did this study set out to answer?

The aim is to evaluate how artificial intelligence and big data improve chronic disease management and to identify challenges and future prospects.

February 28, 2026Open Access

Artificial intelligence and big data applications in chronic disease management: clinical outcomes, challenges, and future directions

Key Points

The aim is to evaluate how artificial intelligence and big data improve chronic disease management and to identify challenges and future prospects.
Conducted a comprehensive search across multiple databases from 2015-2025.
Included empirical, narrative, and systematic studies on AI and big data in chronic disease care.
Appraised twelve studies using validated tools based on study design.
AI models showed predictive accuracies between 88%-96% for various chronic diseases.
Achieved a 25% reduction in hospital readmissions and a 30% decrease in chronic kidney disease progression.
Improved treatment adherence by 25% and identified key technologies involved.

Abstract

pstrongAim: /strongTo synthesize applications of Artificial Intelligence (AI) and big data in chronic disease management, evaluate clinical and economic outcomes, challenges, and future directions./p pstrongMethods: /strongA comprehensive search was conducted across PubMed, Scopus, Web of Science, IEEE Xplore, and CINAHL for studies published between 2015-2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.This review was registered in PROSPERO (CRD420251036828). Inclusion criteria encompassed peer-reviewed empirical, narrative, and systematic studies focused on AI and big data in chronic diseases care. Twelve studies were appraised using validated appraisal tools appropriate to each the study design, which conducted across clinical, technological and public health setting, reflecting application of AI and big data in hospital, digital health platform and diseases management./p pstrongResults: /strongAI models achieved predictive accuracies between 88%-96% across diabetes, Chronic Kidney Disease (CKD), chronic obstructive pulmonary disease (COPD), and cardiovascular conditions. Outcomes included a 25% reduction in readmissions, a 30% decrease in CKD progression, and a 25% improvement in treatment adherence. Technologies included machine learning, electronic health record integration, wearable devices, and multi-omics platforms. Major challenges identified were data fragmentation, bias, and regulatory gaps./p pstrongConclusion: /strongThe AI and big data offer transformative potential for chronic disease management from early prediction to personalized treatment, but realizing this requires addressing data fragmentation, ethical and privacy concerns, and ensuring equity and accountability through transparent, explainable AI./p pstrongKeywords: /strongArtificial Intelligence, big data, chronic disease, machine learning, clinical decision support systems/p pnbsp;/p

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