SynthesisJournal of medical Internet research2025
Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.The Lancet. Digital health · 2026Pooled it
- Mapping patients' and professionals' perceptions of artificial intelligence in radiotherapy: a scoping review.Technical innovations & patient support in radiation oncology · 2026Review
- When is AI "just another innovation"? A comparative conceptual analysis of artificial intelligence and evidence-based practice implementation.Implementation science communications · 2026Article
- AI for Opportunistic Identification of BMD and Bone Microarchitecture: A Narrative Review.Calcified tissue international · 2026Review
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- Preventing fragility fractures: AI-based opportunistic osteoporosis detection in routine CT .Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- Large Language Models for Mental Health Prediction: Scoping Review of Bias and Clinical Utility Documentation in 2019-2024.JMIR AI · 2026Review
- Sociotechnical Misalignments in Hospital AI System Implementation: Qualitative Case Study.Journal of medical Internet research · 2026Article
- Screening for Missed Opportunities for Diagnosis in the ED Using eTriggers and Large Language Models.JAMA network open · 2026Article
- Measuring the Impact of AI on Report-Drafting Efficiency in Chest Computed Tomography Interpretation: Retrospective Analysis.Journal of medical Internet research · 2026Article
- ChatGPT Versus DeepSeek for Breast Cancer Information Retrieval: Quantitative Comparative Study.JMIR cancer · 2026Article
- Comprehensive recommendations for the implementation of artificial intelligence in healthcare: a narrative review on facilitators and barriers.BMJ open quality · 2026Review
- Implementing an Artificial Intelligence Decision Support System in Radiology: Prospective Qualitative Evaluation Study Using the Nonadoption Abandonment Scale-Up, Spread, and Sustainability (NASSS) Framework.Journal of medical Internet research · 2026Article
- Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.Journal of medical Internet research · 2025Observational
- AI-augmented frameworks for enhancing Alzheimer's disease clinical trials: A memory clinic perspective.The journal of prevention of Alzheimer's disease · 2025Article
- Workflow analysis and evaluation of a next-generation phenotyping tool: A qualitative study of Face2Gene.European journal of human genetics : EJHG · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
objectiveThis study aimed to systematically assess and synthesize the facilitators and barriers to AI implementation reported in studies evaluating AI solutions in routine medical imaging.
methodsWe 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.
resultsOur 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.
conclusionsThis 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 registrationPROSPERO CRD42022303439; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022303439. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/40485.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.