Hospitals, Health Systems and Academic Medical Centers

Clinical Research Data Management Software

Unleash the value of your clinical data and scale your research operations to enable improved quality of care and outcomes across your organization with a medical imaging AI development platform.

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Streamline Your Imaging Management and Optimize Your AI Strategy

Flywheel helps you efficiently aggregate, curate, and manage imaging and related data from multiple sources. Our cloud-based solutions help you accelerate outcomes analysis, develop analysis-ready data, and build novel imaging algorithms. With tools to index, ingest, and curate data as well as automate processing and machine learning pipelines, Flywheel is an end-to-end clinical research data management software that provides for secure, regulatory-compliant collaboration.

End-to-End Clinical Research Data Management & AI Development Solutions

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Machine Learning

  • Maximize value with a scalable machine learning solution
  • Streamline labeling, classification, and image annotation
  • Manage blind reader studies
  • Automate processing and training workflows
  • Deploy models for translational testing
  • Provenance to support regulatory approvals
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Secure Multi-Center Collaboration

  • Streamlined discovery of patient cohorts with PACS and EMR integration
  • Index and search metadata and radiology reports
  • Quality controls and automated pre-processing and pipelines
  • Customization via APIs, Python, and Matlab
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Clinical Trials

  • Streamline data collection, processing, and sharing
  • Secure imaging data transfer and verification
  • De-identification workflow
  • Research workflow automation
  • 21 CFR Part 11 compliant data management
  • Connectivity with preferred imaging workstations
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Peter McCaffrey

“Flywheel allows us to extract maximum value out of the assets we already have by standardizing data pipelines across all research projects. We are able to much more quickly access and collaborate on data while feeling confident that everyone is working off a shared, secure dataset that is always current. Our researchers are able to work much more efficiently on artificial intelligence and collaborative research while spending less time managing data.”

Peter McCaffrey, MD

University of Texas Medical Branch Director of Pathology Informatics; Director of Bioinformatics and Artificial Intelligence; Director of Laboratory Information Services; and Assistant Professor of Pathology

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