Check out our article collection for insights from Flywheel on platform updates, how our users are tackling challenges in research data management, and more.
Nicolas directs our team of scientific solutions engineers, working with clients from academia to life sciences to come up with custom solutions to their greatest imaging challenges.
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AI offers the opportunity to automate both routine imaging tasks and imaging analysis. How can you ensure you're developing trustworthy AI? Learn about the techniques and data practices that can help.
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In this white paper, we explore how organizations can follow 21 CFR Part 11 in clinical trials involving imaging data for submitting novel drug-based therapies and creating approvable, usable AI.
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In this white paper, we explore the challenges medical enterprises face in managing medical imaging data and presents strategies for harnessing the transformative power of AI to accelerate medicine.
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AI-enhanced medical imaging in oncology can reveal deeper insights. Challenges include data variability and privacy concerns, but platforms like Flywheel help with data management, sharing & analysis.
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King’s College London and the Bill and Melinda Gates Foundation UNITY Project use Flywheel as a medical image sharing hub for international research into health outcomes and child brain development.
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We use one platform to conduct a comparison of two deep learning liver and kidney segmentation models, revealing the importance of complete DICOM header curation to reduce segmentation errors.
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The growth of AI has raised expectations for good data management and data quality. Learn how to adapt to new standards of data tagging and medical imaging data integration.
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Is it counterproductive for drug developers to squander time and resources piecing together data management infrastructure for AI? Learn about data management realities that pose challenges to AI.
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Radiologists cut 8 months of processing time leveraging Flywheel and NVIDIA. See how they access millions of images, annotate data efficiently, and train and deploy medical imaging AI models.
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See how Flywheel’s platform enables AI in pharma R&D for smarter use of large volumes of imaging & delves into the unique challenges in handling and curating data for model creation.
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Medical imaging holds great potential in life sciences R&D, but it can be time-consuming to leverage. The right data management and computation tools can accelerate breakthroughs and improve outcomes.
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Dr. Peter McCaffrey at UTMB is leveraging Flywheel to enable research collaborations and make AI-ready data sets. Read the details of his unique approach.
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This article from Drug Discovery & Development explains how pharma research teams can unite legacy data, including imaging data, to better characterize disease mechanisms and therapeutic responses.
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The NIH data management and sharing mandates are now in effect. Flywheel offers a complete, enterprise-scale solution for governance and compliance with DMS policies.
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Flywheel's medical director, Aaron Mintz, spoke with Radiology Today about how federated learning holds promise to accelerate breast imaging AI research and improve patient outcomes.
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