Navigating the barriers and facilitators to implementation of AI in healthcare : a scoping review.

Aims

There is increasing emphasis on applying AI techniques to enhance healthcare delivery and decision-making. However, despite much interest and early promise, a major challenge is translation into clinical practice. To address the challenges of AI deployment, optimize implementation, and establish strategies for effective utilization of AI technology in healthcare, we aimed to answer the question: what are the key determinants influencing effective deployment of AI technology in healthcare?

Methods

We followed PRISMA-ScR and the Joanna Briggs Institute Methodology guidelines for scoping reviews; the research protocol was published prospectively on Open Science Framework. We searched PubMed, Cochrane, Ovid MEDLINE, Scopus, and IEEE Xplore for papers published in English from 2000, including systematic/scoping reviews and meta-analyses with full text available.

Results

The initial search was limited to AI medical imaging technology. It identified 1,511 papers, of which 523 met the eligibility criteria based on title and abstract screening. A total of 488 papers were excluded due to context or irrelevant content, leaving 35 papers for full-text review. No systematic/scoping reviews specifically addressing the deployment of AI medical imaging solutions were identified, prompting the inclusion criteria to be broadened to encompass any study designs related to all relevant technology. Overall, 15 papers were included in the final scoping review.

Conclusion

The successful deployment of AI in healthcare is challenging, due to barriers which can be ethical, technological, regulatory, financial, or patient- and workforce-related. Facilitators to drive successful implementation include planning, organizational culture, patient involvement, stakeholder engagement, education, and leadership. Leveraging these essential barriers and facilitators provides a foundation for developing implementation strategies that streamline the deployment of AI technology in healthcare.

COI Statement

The authors declare no conflict of interest. M. Khattak declares an AOUK&I 2021 Major Project grant of £3,500, and National Institute for Health and Care Research (NIHR) Academic Clinical Fellowship funding from the NIHR Academy. D. C. Perry is a NIHR Research Professor, funded by the NIHR Academy during the study (NIHR301655). The views expressed in this report are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. J. S. Bowness reports book royalties from Cambridge University Press, consulting fees from AutonomUS Medical Technologies and Intelligent Ultrasound, and speaker fees from the Belgian Association for Regional Anaesthesia and King Faisal Specialist Hospital & Research Centre, and hospitality from BBraun, all of which are unrelated to the study. J. S. Bowness is also Senior Manager of Clinical Development for GE Healthcare, Chair of the AI Workstream, a member of the Research & Innovation Committee, and a member of the Safety & Standards Committee for the Association of Anaesthetists. P. Kierkegaard and A. McGregor are employed at Imperial College London in a non-clinical research academic role. P. Kierkegaard also reports a Pancreatic Cancer UK grant (2023) and internal seed funding awards, all of which are unrelated to this study.

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