Multimodal AI Algorithms for Risk Early Warning and Proactive Intervention in Community-Based Elderly Care: A Review

Population aging is placing growing pressure on elderly care systems, and artificial intelligence (AI) is increasingly viewed as a potential solution. In community-based elderly care, multimodal AI can integrate sensing, data fusion, risk prediction, explanation, and intervention. Such systems may enable a shift from post-event response to early warning and proactive care. However, existing reviews have often examined these components separately. As a result, the dependencies between different stages—particularly the transition from risk prediction to closed-loop intervention—remain insufficiently explored. To address this gap, this narrative review organizes the literature around an end-to-end framework comprising “sensing–fusion–prediction–explanation–intervention–feedback.” This review includes 94 publications up to 30 June 2026. Each publication was classified as providing either direct or indirect evidence relevant to community-dwelling older adults. This review compares major algorithmic approaches to multimodal fusion and temporal risk prediction across several deployment-related dimensions, including temporal modeling, robustness to missing modalities, calibration, interpretability, validation design, and computational cost. It also examines explainable AI and intervention-generation methods, ranging from post hoc attribution and tiered rule-based alerts to reinforcement learning policies. This review also identifies key challenges at the data, model, intervention, and system levels. It highlights the need for uncertainty-aware, privacy-preserving, and closed-loop solutions. By treating the entire care loop, rather than any single algorithm, as the unit of analysis, this review bridges the gap between component-focused research and the integrated systems required for effective community-based elderly care.