AIGC in an Aging World: Rebuilding Health and Elderly Care from the Ground Up - Li, Changrun (Computers and Artificial Intelligence, 2025)
Population aging and rising health expenditures are forcing health systems to seaxrch for new ways to deliver care with limited human and fiscal resources. The aim of this paper is to analyze how AI-generated content (AIGC), powered by large language and multimodal models, is moving from a “content toy” to a key layer of infrastructure across elderly care, clinical services, wellness consumption, and health policymaking. First, it outlines the basic features of AIGC and explains why it is well-suited to unstructured, multimodal health data. It then examines concrete applications in three front-end domains: smart companionship, remote monitoring, and operational support in elderly care; clinical documentation, chronic disease coaching, and hospital operations in healthcare; and personalized marketing, product strategy, and consumer insight in the dietary supplements sector. The paper further discusses how AIGC-driven analytics can translate fragmented health data into actionable insights for providers, insurers, companies, and regulators. The analysis indicates that AIGC can alleviate documentation burdens, enhance chronic disease self-management, and improve the efficiency of elderly-care operations under tight fiscal constraints. Finally, it highlights major risks—including privacy breaches, bias, hallucinations, deepfake-driven misinformation, unclear liability, and the digital divide for older adults—and argues that robust governance and inclusive design are essential. The conclusion suggests that AIGC can help aging societies keep their health and care systems sustainable, but only if technological innovation is matched by strong ethical and regulatory safeguards.