Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 25, 2024
Date Accepted: May 15, 2025
Reported Facilitators and Barriers to AI Implementation in Routine Medical Imaging: Systematic Review and Qualitative Analysis
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 adoption into clinical workflows.
Objective:
We aimed to systematically assess and synthesize facilitators and barriers to AI implementation reported in studies evaluating AI solutions in routine medical imaging.
Methods:
We conducted a systematic review of six medical databases. Using qualitative content analysis, we extracted 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 adoption.
Results:
Our search yielded 13,760 records. After screening, we included 38 original studies in our final review. We identified 12 dimensions with a total of 37 subthemes influencing AI implementation in healthcare workflows. Key dimensions included Evaluation of AI Use and Fit into Workflow, with considerable frequency depending on the stage of the implementation process. Twenty themes were mentioned as both facilitators and barriers. Studies often focused predominantly on performance metrics over experiences or outcomes of clinicians.
Conclusions:
This systematic review provides a thorough synthesis of facilitators and barriers to successful AI adoption in medical imaging. Our study highlights the usefulness and the fit into routine clinical workflow when implementing AI technologies in clinical care. Most studies did not directly report facilitators and barriers, 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 healthcare. Clinical Trial: Systematic review registration: Prospero ID CRD42022303439 International Registered Report Identifier (IRRID): RR2-10.2196/40485
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