The role of artificial intelligence in health insurance systems for managing multimorbidity (communicable and non-communicable diseases) in older adults: A scoping review

Abstract

The aging population is fueling geriatric multimorbidity—the confluence of communicable and noncommunicable diseases—which is putting strain on health insurance ecosystems. In accordance with the Sustainable Development Goals (SDGs 3 and 10), this assessment looks into the use of Artificial Intelligence (AI) to manage these problems and ensure fair treatment. Following PRISMA-ScR guidelines, a systematic search of important databases (PubMed, Scopus, Web of Science, and EMBASE) was conducted for literature published between 2016 and 2026. Out of 1,245 initial screened data, 15 relevant studies were discovered and examined. AI drives three major innovations: (1) ensemble machine learning algorithms (e.g., XGBoost) outperform traditional actuarial models in predicting claim costs; (2) clinical AI optimizes polypharmacy and speeds up early infectious disease detection; and (3) automated machine learning architectures provide robust claim fraud detection. However, algorithmic bias and opaque usage management strategies for care denial cause severe ethical concerns. While AI increases financial and clinical efficiency, its application must extend beyond cost containment. Robust governance, explainable AI (XAI), and effective human monitoring are essential for safeguarding vulnerable elderly populations and improving health equity.