Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review

Medication non-adherence affects 40%-50% of chronic disease patients globally, causing preventable morbidity and substantial healthcare costs. Traditional adherence monitoring approaches are retrospective and reactive, limiting timely intervention. Artificial intelligence and machine learning offer novel approaches for prospective adherence risk prediction, enabling anticipatory, resource-efficient interventions. This narrative review synthesizes current evidence on AI-based non-adherence prediction across chronic diseases including HIV, tuberculosis, diabetes, hypertension, and mental health disorders. Machine learning models integrating heterogeneous data sources electronic health records, pharmacy refill patterns, sociodemographic variables, and healthcare utilization achieve discrimination metrics (AUC 0.70-0.95) superior to traditional risk stratification. These AUC values are reported descriptively to reflect model discrimination within individual studies and should not be interpreted as results of formal comparison or quantitative synthesis across diseases or modeling approaches. However, significant barriers constrain clinical translation: limited external validation, algorithmic bias affecting marginalized populations, inadequate interpretability, data privacy concerns, and substantial implementation challenges in resource-limited health systems. Future research priorities include rigorous multicenter external validation, model development in low- and middle-income countries, advancement of interpretable architectures, and prospective randomized trials evaluating clinical outcomes. Responsible AI deployment requires participatory governance, health equity prioritization, and maintenance of clinician oversight throughout implementation. This review critically evaluates AI potential while emphasizing prerequisites for equitable, ethical, and clinically meaningful adherence prediction in global health contexts.