常规医学影像中实施人工智能的促进因素和障碍：系统评价与定性分析

Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.

作者信息

Wenderott Katharina, Krups Jim, Weigl Matthias, Wooldridge Abigail R

机构信息

Institute for Patient Safety, University Hospital Bonn, Bonn, Germany.

Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign, Urbana, IL, United States.

出版信息

J Med Internet Res. 2025 Jul 21;27:e63649. doi: 10.2196/63649.

DOI:10.2196/63649

PMID:40690758

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12322614/

Abstract

BACKGROUND

Artificial intelligence (AI) is rapidly advancing in health care, particularly in medical imaging, offering potential for improved efficiency and reduced workload. However, there is little systematic evidence on process factors for successful AI technology implementation into clinical workflows.

OBJECTIVE

This study aimed to systematically assess and synthesize the facilitators and barriers to AI implementation reported in studies evaluating AI solutions in routine medical imaging.

METHODS

We conducted a systematic review of 6 medical databases. Using a qualitative content analysis, we extracted the reported facilitators and barriers, outcomes, and moderators in the implementation process of AI. Two reviewers analyzed and categorized the data separately. We then used epistemic network analysis to explore their relationships across different stages of AI implementation.

RESULTS

Our search yielded 13,756 records. After screening, we included 38 original studies in our final review. We identified 12 key dimensions and 37 subthemes that influence the implementation of AI in health care workflows. Key dimensions included evaluation of AI use and fit into workflow, with frequency depending considerably on the stage of the implementation process. In total, 20 themes were mentioned as both facilitators and barriers to AI implementation. Studies often focused predominantly on performance metrics over the experiences or outcomes of clinicians.

CONCLUSIONS

This systematic review provides a thorough synthesis of facilitators and barriers to successful AI implementation in medical imaging. Our study highlights the usefulness of AI technologies in clinical care and the fit of their integration into routine clinical workflows. Most studies did not directly report facilitators and barriers to AI implementation, underscoring the importance of comprehensive reporting to foster knowledge sharing. Our findings reveal a predominant focus on technological aspects of AI adoption in clinical work, highlighting the need for holistic, human-centric consideration to fully leverage the potential of AI in health care.

TRIAL REGISTRATION

PROSPERO CRD42022303439; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022303439.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/40485.

摘要

背景

人工智能（AI）在医疗保健领域正迅速发展，尤其是在医学成像方面，具有提高效率和减轻工作量的潜力。然而，关于将人工智能技术成功应用于临床工作流程的过程因素，几乎没有系统的证据。

目的

本研究旨在系统评估和综合在评估常规医学成像中人工智能解决方案的研究中报告的人工智能实施的促进因素和障碍。

方法

我们对6个医学数据库进行了系统综述。通过定性内容分析，我们提取了人工智能实施过程中报告的促进因素和障碍、结果以及调节因素。两名评审员分别对数据进行分析和分类。然后，我们使用认知网络分析来探索它们在人工智能实施不同阶段的关系。

结果

我们的检索产生了13756条记录。经过筛选，我们最终纳入了38项原创研究。我们确定了影响人工智能在医疗工作流程中实施的12个关键维度和37个子主题。关键维度包括对人工智能使用的评估以及与工作流程的契合度，其出现频率在很大程度上取决于实施过程的阶段。总共有20个主题被提及为人工智能实施的促进因素和障碍。研究通常主要关注性能指标，而不是临床医生的经验或结果。

结论

本系统综述全面综合了医学成像中成功实施人工智能的促进因素和障碍。我们的研究强调了人工智能技术在临床护理中的有用性以及它们融入常规临床工作流程的契合度。大多数研究没有直接报告人工智能实施的促进因素和障碍，这凸显了全面报告以促进知识共享的重要性。我们的研究结果揭示了在临床工作中采用人工智能时主要关注技术方面，强调需要进行全面的、以人为主的考虑，以充分发挥人工智能在医疗保健中的潜力。