Lambert Sophie Isabelle, Madi Murielle, Sopka Saša, Lenes Andrea, Stange Hendrik, Buszello Claus-Peter, Stephan Astrid
AIXTRA-Competence Center for Training and Patient Safety, Medical Faculty, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
Department of Anesthesiology, Uniklinik RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
NPJ Digit Med. 2023 Jun 10;6(1):111. doi: 10.1038/s41746-023-00852-5.
Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.
人工智能(AI)在医疗保健领域的重要性日益凸显。接受度是人工智能广泛应用的不可或缺的前提条件。本综合综述的目的是探讨影响医疗保健专业人员在医院环境中接受人工智能的障碍和促进因素。42篇文章符合本综述的纳入标准。从纳入的研究中提取了与研究相关的要素,如人工智能的类型、影响接受度的因素以及参与者的职业,并对研究质量进行了评估。数据提取和结果根据技术接受与使用统一理论(UTAUT)模型进行呈现。纳入的研究揭示了医院环境中人工智能接受度的各种促进因素和阻碍因素。临床决策支持系统(CDSS)是大多数研究(n = 21)中包含的人工智能形式。关于人工智能对错误发生、警报敏感性和及时资源影响的认知存在不同结果。相比之下,对(职业)自主权丧失的担忧以及将人工智能整合到临床工作流程中的困难被一致认为是阻碍因素。另一方面,人工智能使用培训促进了接受度。不同的结果可能是由于不同人工智能系统的应用和功能差异以及专业间和跨学科差异造成的。总之,为了促进医疗保健专业人员对人工智能的接受,建议在人工智能开发的早期阶段将最终用户纳入其中,并提供根据需求调整的医疗保健人工智能使用培训以及提供适当的基础设施。