ArticleTomography (Ann Arbor, Mich.)2023
Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review.
Article in Tomography (Ann Arbor, Mich.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
40 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Factors influencing AI acceptance in radiology: a systematic review across the radiology workflow.European radiology · 2026Pooled it
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- From resistance to reliance: A human-centered analysis of the spectrum of radiologists' trust in AI.European journal of radiology open · 2026Article
- AI integration in pediatric radiology: perspectives from international academic leaders.European radiology · 2026Article
- Artificial Intelligence as an Add-On Instrument in Fetal Ultrasound; Sonographers' and Obstetricians' Expectations.Prenatal diagnosis · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Automating the Management of Extraspinal Findings in Magnetic Resonance Imaging Spine Studies Using a Privacy-Preserving Large Language Model: Retrospective Validation Study.Journal of medical Internet research · 2026Article
- External validation of a top-ranked model from the RSNA pulmonary embolism detection challenge: assessment of generalizability.Scientific reports · 2026Article
- Prospective pilot evaluation of a deep learning model for kidney stone detection on CT using a web-based workflow platform.International urology and nephrology · 2026Article
- Investigating the Potential Effects of Medical AI Systems on Physician Autonomy: Pretest of a Semistructured Qualitative Interview Guide.JMIR formative research · 2026Article
- Digital diagnostics, biomarkers and therapeutics in an evolving healthcare system: From promise to practice.British journal of clinical pharmacology · 2026Review
- Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience.Journal of medical Internet research · 2026Article
- Investigating the Clinical Value in Relation to Implementation and Use of an AI-Generated Fracture Algorithm Tool to Support Clinical Decision-Making.Diagnostics (Basel, Switzerland) · 2026Article
- Opportunistic Screening on Chest CT, From theAJR. American journal of roentgenology · 2026Review
- Vendors' perspectives on AI implementation in medical imaging and oncology: a cross-sectional survey.European radiology · 2026Article
- Access and Reimbursement for Artificial Intelligence in Radiology: A Macau Perspective.Korean journal of radiology · 2026Article
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- University students' acceptance of generative artificial intelligence tools: a mixed-methods study on opinions, attitudes, and behavioral intentions.BMC psychology · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician's perspective.
methodsA systematic search was performed using Ovid Medline and Embase. Keywords were used to generate refined queries with the inclusion of computer-aided diagnosis, artificial intelligence, and barriers and enablers. Three reviewers assessed the articles, with a fourth reviewer used for disagreements. The risk of bias was mitigated by including both quantitative and qualitative studies.
resultsAn electronic search from January 2000 to 2023 identified 513 studies. Twelve articles were found to fulfill the inclusion criteria: qualitative studies (
conclusionsThis scoping review found that few studies have been designed specifically to identify barriers and enablers to the acceptance of AI in radiology practice. The majority of studies have assessed the perception of AI replacing radiologists, rather than other barriers or enablers in the adoption of AI. To comprehensively evaluate the potential advantages and disadvantages of integrating AI innovations into radiology practice, gathering more robust research evidence on stakeholder perspectives and attitudes is essential.
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Registered trials
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.