ReviewFrontiers in digital health2026
Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.