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Background: Multimorbidity is increasing the complexity and cost of primary healthcare, while digital technologies may support coordinated, preventive, and person-centered care. We systematically reviewed evidence on digital health and artificial intelligence (AI) interventions for prevention and management of multimorbidity in primary healthcare. Methodology: PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the Cochrane Library were searched using predefined concepts relating to multimorbidity, digital health, artificial intelligence, and primary healthcare. Studies were screened against prespecified eligibility criteria in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020. Data on study characteristics, interventions, and outcomes were extracted, methodological quality was assessed, and comparable quantitative outcomes were synthesized using random-effects meta-analysis. Results: Thirty-three studies involving 23,954 participants were included. Pooled analyses showed reductions in depressive symptoms (standardized mean difference (SMD): −0.36, 95% confidence interval (CI): −0.47 to −0.25; I²: 58%), hospitalization/readmission (risk ratio (RR): 0.81, 95% CI: –0.72 to –0.92; I²: 19%) and diastolic blood pressure (mean difference (MD): −1.7 mmHg, 95% CI: −3.1 to −0.3; I²: 0%), alongside improvements in quality of life, medication appropriateness, self-management/adherence and cardiovascular outcomes. Conclusion: Digital interventions can improve several patient-centered and clinical outcomes in multimorbidity. However, evidence for AI-specific applications remains less developed than evidence for broader digital health, and implementation must address interoperability, digital exclusion, governance, equity, and sustainability.
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Nwokedike et al. (2026) studied this question.
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