Artificial Intelligence–Enabled Home-Based Rehabilitation Management for Patients with Chronic Diseases: A Scoping Review

Chronic diseases represent a major global public health challenge, particularly in rapidly aging societies. In China, population aging is accelerating, and older adults constitute the primary population affected by chronic conditions such as hypertension, diabetes, cardiovascular disease, and cancer. Chronic diseases account for the majority of deaths and impose substantial long-term economic and healthcare burdens. Effective long-term management, particularly in home-based rehabilitation settings, is therefore critical to improving patient outcomes, reducing healthcare utilization, and supporting sustainable health systems. However, traditional home-based chronic disease rehabilitation management models face multiple systemic limitations. These include uneven distribution of healthcare resources, limited access to continuous professional support, low long-term patient adherence, insufficient personalization of interventions, and restricted capacity for dynamic monitoring and timely adjustment of care plans. These challenges highlight the need for innovative care models that enable continuous, individualized, and data-driven management beyond institutional healthcare settings. Recent advances in artificial intelligence (AI) have created new opportunities to transform chronic disease management across the entire continuum of care. AI technologies—including machine learning algorithms, wearable sensor systems, intelligent monitoring platforms, robotics, and generative AI models such as large language models—have demonstrated strong capabilities in large-scale data processing, pattern recognition, predictive analytics, and adaptive decision support. These capabilities may enable early risk detection, precision disease stratification, personalized intervention planning, remote monitoring, behavioral support, and automated health education. Consequently, AI has the potential to significantly enhance home-based rehabilitation management by improving efficiency, accessibility, personalization, and continuity of care. Despite growing interest and rapid technological development, current research on AI-enabled home-based chronic disease rehabilitation management remains fragmented across disciplines and clinical contexts. Evidence regarding application scenarios, implementation models, effectiveness, and practical challenges is dispersed and has not been comprehensively synthesized. Furthermore, important issues such as clinical integration, data governance, ethical considerations, technical validation, user acceptability, and policy implementation require systematic examination. Therefore, the purpose of this scoping review is to systematically map and synthesize the existing body of evidence on artificial intelligence–enabled home-based rehabilitation management for patients with chronic diseases. Specifically, this review aims to: Identify and categorize the types of artificial intelligence technologies applied in home-based rehabilitation management for chronic disease populations. Describe the specific application scenarios and implementation approaches of AI in home-based care, including monitoring, intervention delivery, decision support, rehabilitation guidance, and patient self-management support. Summarize reported outcomes related to clinical effectiveness, patient engagement, adherence, quality of life, healthcare utilization, and system performance. Examine the perceived benefits, limitations, barriers, and risks associated with AI integration in home-based chronic disease management. Identify implementation strategies, best practice recommendations, and policy or regulatory considerations reported in the literature. Map current research gaps and priority directions for future research and practice. This scoping review follows the Population–Concept–Context (PCC) framework. The population includes individuals receiving or delivering home-based rehabilitation for chronic diseases (e.g., patients, nurses, and caregivers). The concept focuses on the application of artificial intelligence technologies in home-based rehabilitation and nursing management. The context encompasses rehabilitation care, psychological support, and health education delivered in home settings or through remote care platforms. A systematic search will be conducted across major international and Chinese databases, including PubMed, Web of Science, Embase, CINAHL, Cochrane Library, CNKI, WanFang Data, VIP, and SinoMed. Two independent reviewers will conduct study selection and data extraction according to predefined eligibility criteria. The expected outcomes of this review include a comprehensive evidence map of AI applications in home-based chronic disease rehabilitation management, a structured synthesis of technological approaches and clinical use cases, and an analysis of implementation challenges and enabling factors. The review will also identify knowledge gaps to inform future research design, clinical innovation, and health policy development. Ultimately, this work aims to provide an evidence-informed reference framework for researchers, clinicians, healthcare administrators, and policy makers. By clarifying the current state of evidence and future directions, this review seeks to support the safe, effective, and equitable integration of artificial intelligence into home-based chronic disease rehabilitation management and to promote the transition toward intelligent, personalized, and system-level care models.