Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications.

Area of Science:

• Artificial Intelligence in Healthcare
• Digital Health
• Computational Medicine

Background:

• Multimorbidity, the co-occurrence of multiple chronic conditions, presents significant challenges in healthcare management.
• High rates of polypharmacy, fragmented care, and lack of integrated strategies complicate multimorbidity management, leading to poor outcomes.

Purpose of the Study:

• To explore the potential role of artificial intelligence (AI) in improving the management of patients with multimorbidity.
• To evaluate AI's contributions to diagnosis, treatment optimization, and care coordination for individuals with multiple chronic conditions.

Main Methods:

• A narrative review synthesizing literature on AI applications in healthcare, focusing on multimorbidity.
• Literature search conducted on PubMed and Scopus using keywords such as "multimorbidity," "chronic disease management," and "machine learning."
• Synthesis of studies on AI's role in diagnosis, risk prediction, clinical decision support, and personalized care.

Main Results:

• AI shows promise in early detection, accurate diagnosis, personalized treatment planning, and improved care coordination for multimorbidity.
• Potential benefits include better clinical outcomes, increased efficiency, cost savings, and more patient-centered care.
• Challenges such as data privacy, algorithmic bias, and ethical concerns hinder widespread AI adoption.

Conclusions:

• AI holds transformative potential for managing the complexities of multimorbidity.
• Future efforts should prioritize responsible AI integration, ethical frameworks, and interdisciplinary collaboration.
• Harnessing AI can lead to high-quality, coordinated, and personalized care for patients with multimorbidity.