ArticleJCO clinical cancer informatics2020
Joint Imaging Platform for Federated Clinical Data Analytics.
Article in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- APOLLO11: a bio-data-driven model for clinical and translational research in lung cancer.NPJ precision oncology · 2026Article
- Radiological data processing system: lifecycle management and annotation.International journal of computer assisted radiology and surgery · 2025Article
- Exploration of Fully-Automated Body Composition Analysis Using Routine CT-Staging of Lung Cancer Patients for Survival Prognosis.Journal of cachexia, sarcopenia and muscle · 2025Article
- Integrating an AI platform into clinical IT: BPMN processes for clinical AI model development.BMC medical informatics and decision making · 2025Article
- Multi-modal dataset creation for federated learning with DICOM-structured reports.International journal of computer assisted radiology and surgery · 2025Article
- Real world federated learning with a knowledge distilled transformer for cardiac CT imaging.NPJ digital medicine · 2025Article
- Federated learning for lesion segmentation in multiple sclerosis: a real-world multi-center feasibility study.Frontiers in neurology · 2025Article
- Article
- Value of MRI - T2 Mapping to Differentiate Clinically Significant Prostate Cancer.Journal of imaging informatics in medicine · 2024Article
- Accelerating Parkinson's Disease drug development with federated learning approaches.NPJ Parkinson's disease · 2024Review
- The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions.Scientific reports · 2024Article
- AI-derived body composition parameters as prognostic factors in patients with HCC undergoing TACE in a multicenter study.JHEP reports : innovation in hepatology · 2024Article
- CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology.Radiology · 2024Review
- Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology.Healthcare (Basel, Switzerland) · 2023Article
- Clinical Informatics Approaches to Facilitate Cancer Data Sharing.Yearbook of medical informatics · 2023Review
- Federated benchmarking of medical artificial intelligence with MedPerf.Nature machine intelligence · 2023Article
- Harmonization Strategies in Multicenter MRI-Based Radiomics.Journal of imaging · 2022Review
- AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine.Healthcare (Basel, Switzerland) · 2022Article
- Deep Learning-based Assessment of Oncologic Outcomes from Natural Language Processing of Structured Radiology Reports.Radiology. Artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
61 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeImage analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles.
methodsThe Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions.
resultsThe JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform.
conclusionThe results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.
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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.