SynthesisRadiology2022
Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.
Open access · greenAbstract readMeta-AnalysisSystematic Review
In one paragraph
Synthesis in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 5 of them syntheses that pooled it.
0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 5 pooled it
11.6field-weighted citation impact, top 1% of its field
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.
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Who cites it
49 citing papers in PubMed, 5 syntheses or guidelines pooled it, 114 citations in OpenAlex.
- Use of artificial intelligence in education and training of radiology.Frontiers in radiology · 2026Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.Clinical and experimental medicine · 2023Pooled it
- Pooled it
- The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Optimizing AI-assisted decision-making in mammography screening: focusing on uncertainties.Insights into imaging · 2026Article
- Ten Years of Artificial Intelligence in Screening Mammography: A Systematic Review and Meta-Analysis of Diagnostic Accuracy and Clinical Implementation (Literature Published 2015-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Implementation of an artificial intelligence-based system for mammography in the compulsory medical insurance program: results of a 3-year study.Quantitative imaging in medicine and surgery · 2026Article
- AI for Anterior Segment Disease Using Transfer Learning: Adapting Slit-Lamp-Trained Model for Analysis of Smartphone Corneal Images.Translational vision science & technology · 2026Article
- Artificial Intelligence in Early Breast Cancer Detection: A Systematic Review of Innovations in Preventive Women's Healthcare.Healthcare (Basel, Switzerland) · 2026Review
- Accelerating Evidence Synthesis: A BERT-Assisted Workflow for Meta-Analyses of Radiotherapy Complications in Nasopharyngeal Carcinoma.Reports (MDPI) · 2026Article
- Artificial intelligence for breast cancer prevention: the vision ahead.European radiology · 2026Review
- Interpretable machine learning for predicting major amputation risk in hospitalized diabetic foot ulcer patients: a single-center study with temporal external validation.Frontiers in endocrinology · 2026Article
- Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study.JMIR formative research · 2025Article
- Adherence to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM): an umbrella review with a comprehensive two-level analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs.NPJ digital medicine · 2025Article
- Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making.Scientific reports · 2025Article
- Influence of AI Decision Support on Radiologists' Performance and Visual Search in Screening Mammography.Radiology · 2025Article
- Annotation-efficient, patch-based, explainable deep learning using curriculum method for breast cancer detection in screening mammography.Insights into imaging · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 4 countries.
Sarah E HickmanFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0002-4637-7300
Ramona WoitekFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0002-9146-9159
Elizabeth Phuong Vi LeFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0002-3065-1627
Yu Ri ImFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).
Carina Mouritsen LuxhøjFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0001-6901-6877
Angelica I Aviles-RiveroFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0002-8878-0325
Gabrielle C BaxterFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0002-8242-1559
James W MacKayFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0001-7558-3800
Fiona J GilbertFrom the Department of Radiology (S.E.H., R.W., G.C.B., J.W.M., F.J.G.) and Department of Medicine (E.P.V.L., Y.R.I., C.M.L.), University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, England; Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service Foundation Trust, Cambridge, England (R.W., F.J.G.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (R.W.); Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, Cambridge, England (A.I.A.R.); and Norwich Medical School, University of East Anglia, Norwich, England (J.W.M.).ORCID 0000-0002-0124-9962
University of Cambridge · GBNational Health Service · GBUniversity of East Anglia · GB
Funding
Cancer Research UK 26884Cancer Research UK C543/A26884Cancer Research UK C9685/A25177
Abstract
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for screening mammography workflow. Materials and Methods Ovid Embase, Ovid Medline, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science literature databases were searched for relevant studies published from January 2012 to September 2020. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42019156016). Stand-alone technology was defined as a ML algorithm that can be used independently of a human reader. Studies were quality assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 and the Prediction Model Risk of Bias Assessment Tool, and reporting was evaluated using the Checklist for Artificial Intelligence in Medical Imaging. A primary meta-analysis included the top-performing algorithm and corresponding reader performance from which pooled summary estimates for the area under the receiver operating characteristic curve (AUC) were calculated using a bivariate model. Results Fourteen articles were included, which detailed 15 studies for stand-alone detection (
Indexed as
Machine LearningWorkflowBreast NeoplasmsFemaleHumansMammographyRadiographic Image Interpretation, Computer-AssistedSensitivity and Specificity
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Read underepoch 390
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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.