ReviewEClinicalMedicine2024
Stakeholder perspectives towards diagnostic artificial intelligence: a co-produced qualitative evidence synthesis.
Review in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Assessment of perceived objectivity of clinical evaluation methods in surgical education: an approach using a digital platform.Future science OA · 2026Article
- Patients' and Providers' Attitudes Toward Artificial Intelligence and Electronic Health Record Use in Deep Phenotyping and Rare-Disease Screening: An Empty Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Patients' Perspectives on the Implementation of AI in Radiological Diagnostics: Focus Group Study.Journal of medical Internet research · 2026Article
- Virtual Reality Implementation in Mental Health Care Is a Marathon, Not a Sprint: Qualitative Longitudinal Study of a Virtual Reality Training Program.JMIR mental health · 2026Article
- Vendors' perspectives on AI implementation in medical imaging and oncology: a cross-sectional survey.European radiology · 2026Article
- A machine learning-based risk prediction model for early preterm birth: development and prospective validation.Frontiers in medicine · 2026Article
- Public perceptions of health data sharing for artificial intelligence research: a qualitative focus group study in the UK.BMJ digital health & AI · 2026Article
- Radiology Staff Experiences With Integrating Artificial Intelligence Into Radiology Practice in a Swedish Hospital: Qualitative Case Study.JMIR formative research · 2025Article
- Benchmarking transformer-based models for medical record de-identification in a single center multi-specialty evaluation.iScience · 2025Article
- Exploring patient and clinician opinions, perspectives and acceptance of the use of artificial intelligence in the histological diagnosis of prostate cancer.BJUI compass · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Sociotechnical influences on the adoption and use of AI-enabled clinical decision support systems in ophthalmology: a theory-based interview study.BMC health services research · 2025Article
- A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements.Current opinion in anaesthesiology · 2025Review
- The Practical, Robust Implementation and Sustainability (PRISM)-capabilities model for use of Artificial Intelligence in community-engaged implementation science research.Implementation science : IS · 2025Article
- Perspectives on AI and Novel Technologies Among Older Adults, Clinicians, Payers, Investors, and Developers.JAMA network open · 2025Article
- An interpretable machine learning model based on optimal feature selection for identifying CT abnormalities in patients with mild traumatic brain injury.EClinicalMedicine · 2025Article
- Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic.Insights into imaging · 2025Article
- Perceptions of health data commodification in AI-driven healthcare systems in Saudi Arabia.Frontiers in artificial intelligence · 2025Article
- Between Surveillance and Support: A Qualitative Study of Tuberculosis Patients' Expectations and Concerns About AI-Assisted Remote Health Services in China.Patient preference and adherence · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Diagnosis is a cornerstone of medical practice. Worldwide, there is increased demand for diagnostic services, exacerbating workforce shortages. Artificial intelligence (AI) technologies may improve diagnostic efficiency, accuracy, and access. Understanding stakeholder perspectives is key to informing implementation of complex interventions. We systematically reviewed the literature on stakeholder perspectives on diagnostic AI, including all English-language peer-reviewed primary qualitative or mixed-methods research. Methods: We searched PubMed, Ovid MEDLINE/Embase, Scopus, CINAHL and Web of Science (22/2/2023 and updated 8/2/2024). The Critical Appraisal Skills Programme Checklist informed critical appraisal. We used a 'best-fit' framework approach for analysis, using the Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. This study was pre-registered (PROSPERO CRD42022313782). Findings: We screened 16,577 articles and included 44. 689 participants were interviewed, and 402 participated in focus groups. Four stakeholder groups were described: patients, clinicians, researchers and healthcare leaders. We found an under-representation of patients, researchers and leaders across articles. We summarise the differences and relationships between each group in a conceptual model, hinging on the establishment of trust, engagement and collaboration. We present a modification of the NASSS framework, tailored to diagnostic AI. Interpretation: We provide guidance for future research and implementation of diagnostic AI, highlighting the importance of representing all stakeholder groups. We suggest that implementation strategies consider how any proposed software fits within the extended NASSS-AI framework, and how stakeholder priorities and concerns have been addressed. Funding: RK is supported by an NIHR Doctoral Research Fellowship grant (NIHR302562), which funded patient and public involvement activities, and access to Covidence.
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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.