ReviewThe British journal of radiology2023
Federated learning for medical imaging radiology.
Review in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- Decoupling Structural and Vascular Biomarkers in Contrast-Enhanced Mammography: A Methodological Synthesis and Perspective.Diagnostics (Basel, Switzerland) · 2026Article
- Model Based on Ultrasound Radiomics and Machine Learning for Differentiating Uterine Fibroids and Adenomyomas.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Impact of CT slice thickness reduction algorithm on AI-based lung nodule detection in chest CTs of colorectal cancer patients.Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning-Assisted MRI for Differentiating Parotid Gland Tumors: Comparison with FNAB and Radiologic Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.International dental journal · 2026Review
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Federated continual learning for privacy-preserving chest radiograph classification.Scientific reports · 2026Article
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.Scientific reports · 2026Article
- Review
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.Ophthalmology science · 2026Article
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Gas Vesicles and Acoustic Protein Nanostructures in Molecular Ultrasound Nanomedicine: Translational Archetypes, Biomaterial Design, and Barriers to Clinical Realization.International journal of nanomedicine · 2026Review
- Multimodal Deep Learning for Diabetic Retinopathy: A Survey.Journal of ophthalmology · 2026Review
- Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.Frontiers in radiology · 2026Article
- A personalized communication efficient federated learning framework with low rank adaptation for intelligent leukemia diagnosis.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the review outlines the future research directions considering various factors such as data, learning models, system design, governance, and human-in-loop to translate the SOTA into SOTP and effectively collaborate across multiple institutions.
Indexed as
Identifiers
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.