Transforming tai ji quan into next-generation, evidence-based exercise medicine: A translational roadmap for falls prevention.

A substantial body of research provides compelling evidence that tai ji quan, a mind-body exercise rooted in traditional Chinese medicine, is effective in reducing falls and improving mobility and has gained increasing recognition as a form of evidence-based exercise medicine within mainstream healthcare. Despite this strong evidence base and growing policy support, the translation of tai ji quan from research into routine clinical practice remains limited. Addressing this evidence-to-practice gap will require innovative translational strategies that more effectively integrate evidence-based tai ji quan with evolving models of healthcare delivery and emerging digital health technologies. This article reviews the current state of tai ji quan research in falls prevention and proposes an integrated translational roadmap to advance its adoption as a routinely prescribed, clinically embedded, technology-driven intervention. The roadmap is built upon 3 interrelated pillars: embedding tai ji quan within routine clinical workflows, expanding evidence generation through decentralized virtual trial designs, and augmenting implementation through responsibly governed artificial intelligence (AI)-enabled tools and systems. The framework presented may also serve as a generalizable model for applying evidence-based exercise medicine to the prevention, treatment, and management of other chronic conditions associated with aging.