ArticleJournal of the American Medical Informatics Association : JAMIA2023
Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 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
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.Health technology assessment (Winchester, England) · 2026Pooled it
- Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Eye-Tracking Evidence That Verifiable Explanations Support Visual Evidence Checking in AI-Assisted Chest Radiograph Interpretation.Journal of eye movement research · 2026Article
- 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
- Implementing AI innovation in radiology departments in the English NHS: a qualitative study on the experiences of professionals, patient groups and innovators.Frontiers in digital health · 2026Article
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Implementation, Experiences, Impact, and Costs of Artificial Intelligence in Chest Diagnostics: Protocol for a Mixed Methods Evaluation.JMIR research protocols · 2025Article
- Large Language Model-Based Critical Care Big Data Deployment and Extraction: Descriptive Analysis.JMIR medical informatics · 2025Article
- Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development.Frontiers in digital health · 2025Article
- Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.JMIR human factors · 2024Article
- Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel.Journal of medical Internet research · 2024Article
- Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis.EClinicalMedicine · 2024Review
- Digital health interventions use cases: Classification and taxonomy development, a scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesArtificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. MATERIALS AND
methodsWe performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework.
resultsWe conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. DISCUSSION: Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure.
conclusionThe socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.
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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.