@文章{信息:doi/10.2196/36553，作者=“Jovanovic, Mladjan和Mitrov, Goran和Zdravevski, Eftim和Lameski, Petre和Colantonio, Sara和Kampel, Martin和Tellioglu, Hilda和flores - revuelta, Francisco”，标题=“环境辅助生活:人工智能模型、领域、技术和关注的范围审查”，期刊=“J Med Internet Res”，年=“2022”，月=“11”，日=“4”，卷=“24”，数量=“11”，页=“e36553”，关键词=“环境辅助生活;光芒四射;辅助生活;活跃的生活;数字健康;数字健康;自动学习方法;人工智能算法;以人为中心的人工智能;审查; implications; artificial intelligence; mobile phone", abstract="Background: Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)---infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective: This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods: This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results: We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions: This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display{\_}record.php?ID=CRD42022347590 ", issn="1438-8871", doi="10.2196/36553", url="//www.mybigtv.com/2022/11/e36553", url="https://doi.org/10.2196/36553", url="http://www.ncbi.nlm.nih.gov/pubmed/36331530" }