Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications

Abstract

Background: Multimorbidity, the co-occurrence of two or more chronic conditions is increasingly common and poses significant clinical and system-level challenges. Its management is complicated by high rates of polypharmacy, fragmented care, and the lack of integrated treatment strategies, often resulting in poor health outcomes and increased burden on healthcare systems, providers, patients, and caregivers. Aim: This review aims to explore the potential role of artificial intelligence (AI) in improving the management of patients with multimorbidity and to evaluate its contributions to diagnosis, treatment optimization, and care coordination. Methods: A narrative review was conducted to synthesize the literature on AI applications in healthcare, with particular emphasis on their relevance and utility in managing multimorbidity. The literature search was performed using two major electronic databases: PubMed and Scopus. "multimorbidity," "chronic disease management," "machine learning," "deep learning," "predictive analytics," and "computable phenotypes." Relevant studies addressing AI's role in diagnosis, risk prediction, clinical decision support, and personalized care for patients with multiple chronic conditions were identified, reviewed, and synthesized into major thematic areas, including AI capabilities, clinical integration, patient-centered care, and ethical considerations. Results: AI demonstrates promising potential in multimorbidity care through enhanced early detection, accurate diagnosis, development of personalized treatment plans, and improved care coordination. Its implementation may lead to better clinical outcomes, greater efficiency, cost savings, and more patient-centered healthcare delivery. However, challenges such as data privacy, algorithmic bias, and ethical concerns remain important barriers to widespread adoption. Conclusion: AI holds transformative potential for addressing the complexities of multimorbidity management. Future research and policy efforts should focus on responsible integration, ethical frameworks, and interdisciplinary collaboration to harness AI's full potential in delivering high-quality, coordinated, and personalized care for patients with multimorbidity.

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