Amidst national initiatives to advance sports industry development and the progressive integration of information technologies in athletics, artificial intelligence (AI) has emerged as a transformative force in sports science. Concurrently, the "Rehabilitation-Performance Training Integration" paradigm has gained international scholarly attention, dismantling disciplinary barriers between sports rehabilitation and functional training. This interdisciplinary approach addresses the imperative for accelerated athlete recovery post-injury, representing an evolutionary synthesis of sports science and competitive athletics. Leveraging multimodal data learning, AI-driven systems enable precise injury assessment, rehabilitation initiation determination, and dynamic personalized rehabilitation programming. The technology further facilitates intelligent rehabilitation-performance training transitions while establishing longitudinal physical monitoring and electronic health records (EHRs) to construct comprehensive health profiles. In the post-pandemic era, emphasizing "Active Health" initiatives, AI's role in bridging rehabilitation-to-performance transitions assumes critical significance. This study employs literature review and logical analysis methodologies, focusing on AI-driven innovations in post-injury rehabilitation assessment and Rehabilitation-Performance Training transitions. It explores innovative frameworks to advance theoretical paradigms and propose novel approaches with evidence-based references for intelligent Rehabilitation-Performance Training protocols. AI demonstrates substantial efficacy in bridging rehabilitation and performance training phases. However, implementation challenges persist regarding data security, ethical considerations, and the necessity for large-scale clinical databases to optimize algorithmic performance. With technological advancements and growing societal emphasis on Active Health, AI exhibits significant potential in sports injury rehabilitation-performance integration. Critical priorities include enhancing model generalizability, addressing clinical implementation barriers, and establishing robust databases. Emerging technologies such as flexible electronics and neuromorphic computing, when integrated with AI, may enable next-generation "AI Rehabilitation Coaches" and human performance enhancement systems. Strategic development of AI-rehabilitation-performance synergies will accelerate recovery timelines, advance Active Health objectives, and elevate population health metrics.
Zhenyu Fang (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: