Sarcopenia is a progressive, age-related skeletal muscle disorder that serves as a driver of frailty, falls, and mortality in older adults. Despite the recent paradigm shift introduced by the latest sarcopenia consensus, which emphasizes early, proactive detection, sarcopenia cases frequently evade traditional screening due to inherent diagnostic bottlenecks and resource limitations. Artificial intelligence has emerged as a transformative solution to dismantle these barriers across the entire continuum of sarcopenia care. This review explores the rapid evolution of artificial intelligence, beginning with automated opportunistic screening that extracts prognostic musculoskeletal data from routine imaging and electronic health records, advancing toward high-precision multimodal assessment architectures. Beyond initial assessment, artificial intelligence is actively restructuring longitudinal care by the integration of ubiquitous wearables, Large Language Models, and computer vision, enabling dynamic exercise prescriptions and real-time kinematic postural correction for sarcopenia rehabilitation. Realizing this potential requires the medical community to confront urgent clinical barriers, including multi-center validation, semantic interoperability, health economic justification, and the strict preservation of human-centric ethics. By addressing these challenges, this review provides a definitive roadmap for embedding artificial intelligence across the entire sarcopenia care pathway, transforming isolated instances of opportunistic screening into a unified ecosystem for intelligent, proactive rehabilitation.
Wang et al. (Mon,) studied this question.