From Molecular Pathways to Artificial Intelligence: Advancing the Understanding and Management of Osteosarcopenia

Abstract Osteosarcopenia, defined as the coexistence of low bone mass and sarcopenia, is a multifactorial condition influenced by molecular crosstalk between bone and muscle. Growing evidence suggests that this interaction contributes to disease progression and highlights the need for integrated diagnostic and therapeutic approaches. Artificial intelligence (AI) is emerging as an important tool for the clinical management of osteosarcopenia, with potential applications in risk prediction, early diagnosis, evaluation of adverse outcomes such as fractures and falls, imaging analysis, and personalized treatment planning through machine learning and deep learning algorithms or with the support of large language models. This narrative review summarizes current evidence on the pathophysiological mechanisms underlying osteosarcopenia, with particular emphasis on the molecular mediators involved, and examines the current and potential applications of AI in its clinical assessment and management. By integrating advances in musculoskeletal biology with AI-driven approaches, this review highlights emerging opportunities to improve early detection, optimize clinical decision-making, and support the development of precision medicine strategies for patients with osteosarcopenia.