@文章{info:doi/ 10.2199 /40238，作者="Sharma, Malvika和Savage, Carl和Nair, Monika和Larsson, Ingrid和Svedberg, Petra和Nygren, Jens M"，标题="人工智能在医疗保健实践中的应用:范围综述"，期刊="J医学互联网研究"，年="2022"，月="10"，日="5"，卷="24"，数="10"，页="e40238"，关键词="人工智能;卫生保健;实施;范围审查;背景:人工智能(AI)通常被认为是一种潜在的颠覆者，它将改变医学实践。在医疗保健领域收集和获得的数据量，加上计算能力的进步，促进了人工智能的进步和出版物的指数级增长。然而，人工智能应用程序的开发并不能保证它们被应用到日常实践中。有一种风险是，尽管投入了资源，但如果不更好地理解人工智能的实施，就无法实现患者、工作人员和社会的利益。目的:本研究的目的是通过回答3个问题来探讨人工智能在医疗保健实践中的实施是如何在文献中被描述和研究的:人工智能在实践中实施的研究的特点是什么?描述了人工智能系统的类型和应用? What characteristics of the implementation process for AI systems are discernible? Methods: A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results: Of the 9218 records retrieved, 45 (0.49{\%}) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71{\%}) were published recently, were from high-income countries (33/45, 73{\%}), and were intended for care providers (25/45, 56{\%}). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53{\%}) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35{\%}) or related to technical and computational aspects of AI systems (11/45, 24{\%}). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions: Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection. ", issn="1438-8871", doi="10.2196/40238", url="//www.mybigtv.com/2022/10/e40238", url="https://doi.org/10.2196/40238", url="http://www.ncbi.nlm.nih.gov/pubmed/36197712" }