%0期刊文章%@ 1438- 8871% I JMIR出版物%V 24卡塔尔世界杯8强波胆分析% N 11% P e36553% T环境辅助生活:人工智能模型，领域，技术和关注范围综述%A Jovanovic,Mladjan %A Mitrov,Goran %A Zdravevski,Eftim %A Lameski,Petre %A Colantonio,Sara %A Kampel,Martin %A Tellioglu,Hilda %A flores - revuelta,Francisco %+ Singidunum大学计算机科学系，Danijelova 32，贝尔格莱德，11000，塞尔维亚，381 603831844，mjovanovic@singidunum.ac.rs %K环境辅助生活%K AAL %K辅助生活%K主动生活%K数字健康%K数字健康%K自动学习方法%K人工智能算法%K以人为中心的AI %K回顾%K含义%K人工智能%K手机%D 2022 %7 4.11.2022 %9回顾%J J医学互联网Res %G英语%X背景:环境辅助生活(AAL)是各种人工智能(AI)应用程序和平台的统称，可为从健康到日常生活等多种活动中需要帮助的用户提供支持。这些系统使用不同的方法来了解用户，并做出自动决策，即人工智能模型，以个性化服务和提高结果。鉴于为不同需求、健康状况和技术倾向的人开发和部署的众多系统，获得关于所使用的人工智能模型的清晰和全面的见解，以及它们的领域、技术和关注点，以确定未来工作的有前景的方向是至关重要的。目的:本研究旨在对AAL中AI模型的文献进行范围综述。特别地，我们分析了AАL系统中使用的特定AI模型、模型的目标领域、使用模型的技术以及从最终用户的角度考虑的主要问题。我们的目标是巩固这一主题的研究，并告知最终用户、医疗保健专业人员和提供者、研究人员和从业人员，以开发、部署和评估未来的智能AAL系统。方法:本研究采用范围综述的方法，对相关文献进行识别、分析和提取。 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 %M 36331530 %R 10.2196/36553 %U //www.mybigtv.com/2022/11/e36553 %U https://doi.org/10.2196/36553 %U http://www.ncbi.nlm.nih.gov/pubmed/36331530