ArticleRadiographics : a review publication of the Radiological Society of North America, Inc2023
Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data.
Article in Radiographics : a review publication of the Radiological Society of North America, Inc, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research on machine learning-based clinical prediction models: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- Neurofilament Light Chain (NfL) in Neurodegenerative Diseases: Biological and Clinical Significance, Multi-Omics Integration, and AI-Driven Biomarker Modeling for Precision Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Privacy-Preserving Federated Deep Learning for Robust Anomaly Detection in Distributed Security Sensing Systems.Sensors (Basel, Switzerland) · 2026Article
- An interpretable whole-stream video based deep learning framework for surgical skill assessment of basic procedural elements.International journal of surgery (London, England) · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Data mining in pediatric radiology in the era of artificial intelligence.Pediatric radiology · 2026Review
- Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers.Frontiers in public health · 2026Review
- Large language models and large concept models in radiology: Present challenges, future directions, and critical perspectives.World journal of radiology · 2025Review
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Best Practices for the Safe Use of Large Language Models and Other Generative AI in Radiology.Radiology · 2025Review
- A Comprehensive Survey of Deep Learning Approaches in Image Processing.Sensors (Basel, Switzerland) · 2025Review
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis.Neurobiology of disease · 2023Article
- Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.Breast cancer research : BCR · 2023Review
- The Role of Federated Learning Models in Medical Imaging.Radiology. Artificial intelligence · 2023Article
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
Deep learning (DL) algorithms have shown remarkable potential in automating various tasks in medical imaging and radiologic reporting. However, models trained on low quantities of data or only using data from a single institution often are not generalizable to other institutions, which may have different patient demographics or data acquisition characteristics. Therefore, training DL algorithms using data from multiple institutions is crucial to improving the robustness and generalizability of clinically useful DL models. In the context of medical data, simply pooling data from each institution to a central location to train a model poses several issues such as increased risk to patient privacy, increased costs for data storage and transfer, and regulatory challenges. These challenges of centrally hosting data have motivated the development of distributed machine learning techniques and frameworks for collaborative learning that facilitate the training of DL models without the need to explicitly share private medical data. The authors describe several popular methods for collaborative training and review the main considerations for deploying these models. They also highlight publicly available software frameworks for federated learning and showcase several real-world examples of collaborative learning. The authors conclude by discussing some key challenges and future research directions for distributed DL. They aim to introduce clinicians to the benefits, limitations, and risks of using distributed DL for the development of medical artificial intelligence algorithms.
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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.