背景：医疗保健中的人工智能（AI）用例正在增加，有可能提高运营效率和护理结果。然而,将人工智能转化为实用，日常使用受到限制，由于其有效性取决于临床医生的成功实施和采用，病人,和其他医疗保健利益相关者。
目标：由于采用是创新成功扩散的关键因素，本范围审查旨在概述在医疗保健中采用人工智能的障碍和促进因素。
方法：使用JoannaBriggs研究所提供的指导以及Arksey和O\'Malley提出的框架进行了范围审查。MEDLINE,IEEEXplore,搜索了ScienceDirect数据库，以确定英文出版物，这些出版物报告了在医疗保健中采用人工智能的障碍或促进因素。这篇评论的重点是2011年1月至2023年12月发表的文章。该审查对医疗保健环境（医院或社区）或人口（患者，临床医生,医师,或医疗保健管理员)。对选定的文章进行了主题分析，以绘制与医疗保健中采用人工智能的障碍和促进者相关的因素。
结果：在最初的搜索中总共确定了2514篇文章。在标题和摘要评论之后，50(1.99%)篇文章被纳入最终分析。对这些文章进行了审查，以了解医疗保健中采用人工智能的障碍和促进因素。大多数文章都是实证研究，文献综述，reports,和思想文章。确定了大约18类障碍和促进者。这些是按顺序组织的，以提供AI开发的考虑因素，实施，以及促进采用所需的整体结构。
结论：文献综述表明，信任是采用的重要催化剂，发现它受到本综述中确定的几个障碍的影响。治理结构可以是一个关键的促进者，其中,确保所有被确定为障碍的要素得到适当解决。研究结果表明，人工智能在医疗保健中的实施仍然存在，在许多方面,有赖于建立监管和法律框架。进一步研究治理和实施框架的结合，模型，或理论，以增强信任，将具体实现采用是必要的，为那些将人工智能研究转化为实践提供必要的指导。未来的研究也可以扩大到包括试图理解病人对复杂的观点，高风险AI用例以及AI应用程序的使用如何影响临床实践和患者护理，包括社会技术因素，随着更多算法在实际临床环境中实现。
BACKGROUND: Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders.
OBJECTIVE: As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care.
METHODS: A scoping
review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O\'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This
review focused on articles published between January 2011 and December 2023. The
review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care.
RESULTS: A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption.
CONCLUSIONS: The literature
review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this
review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients\' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.
