ReviewBritish journal of cancer2021
Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations.
Review in British journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
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
47 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.Journal of medical Internet research · 2025Pooled it
- Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Optimizing AI-assisted decision-making in mammography screening: focusing on uncertainties.Insights into imaging · 2026Article
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Artificial intelligence in breast cancer imaging: a systematic review of diagnostic performance, predictive modeling, and clinical translation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.Healthcare (Basel, Switzerland) · 2026Review
- Health economics evaluation of artificial intelligence in the field of oncology: a scoping review.Health economics review · 2026Review
- Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.Diagnostics (Basel, Switzerland) · 2026Review
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Patient perspectives on artificial intelligence in mammography interpretation: a comparative survey study of safety-net and academic hospital settings.Breast cancer research and treatment · 2025Article
- Governing Artificial Intelligence in Radiology: A Systematic Review of Ethical, Legal, and Regulatory Frameworks.Diagnostics (Basel, Switzerland) · 2025Review
- Patient Perception of Artificial Intelligence Use in Interpretation of Screening Mammograms: A Survey Study.Radiology. Imaging cancer · 2025Article
- Evolution of an Artificial Intelligence-Powered Application for Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Advances in breast cancer diagnosis: a comprehensive review of imaging, biosensors, and emerging wearable technologies.Frontiers in oncology · 2025Review
- BC-predict: mining of signal biomarkers and production of models for early-stage breast cancer subtyping and prognosis.Frontiers in bioinformatics · 2025Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review.JMIR human factors · 2024Article
- AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.Radiology. Imaging cancer · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Retrospective studies have shown artificial intelligence (AI) algorithms can match as well as enhance radiologist's performance in breast screening. These tools can facilitate tasks not feasible by humans such as the automatic triage of patients and prediction of treatment outcomes. Breast imaging faces growing pressure with the exponential growth in imaging requests and a predicted reduced workforce to provide reports. Solutions to alleviate these pressures are being sought with an increasing interest in the adoption of AI to improve workflow efficiency as well as patient outcomes. Vast quantities of data are needed to test and monitor AI algorithms before and after their incorporation into healthcare systems. Availability of data is currently limited, although strategies are being devised to harness the data that already exists within healthcare institutions. Challenges that underpin the realisation of AI into everyday breast imaging cannot be underestimated and the provision of guidance from national agencies to tackle these challenges, taking into account views from a societal, industrial and healthcare prospective is essential. This review provides background on the evaluation and use of AI in breast imaging in addition to exploring key ethical, technical, legal and regulatory challenges that have been identified so far.
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What OpenQuestion holds
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.