Flywheel

Flywheel is a medical imaging data management and analysis platform designed to help researchers, healthcare organizations, and AI developers maximize the value of their imaging data. Originally born out of Stanford University's Center for Cognitive and Neurobiological Imaging, Flywheel was founded in 2015 and has since grown into a comprehensive enterprise solution serving pharmaceutical and biotech companies, clinical researchers, medical device developers, academic medical centers, and AI innovators worldwide.

The platform addresses a well-known challenge in data science — that roughly 80% of a data scientist's time is spent finding, curating, and organizing data. Flywheel automates these time-consuming processes so that scientists and researchers can focus on making discoveries. Extensible and scalable by design, Flywheel enables users to securely discover, manage, curate, and compute large amounts of imaging data, both within their own organizations and in collaboration with partners across the globe.

Who Flywheel Serves

• Pharmaceutical and Biotech Researchers: Flywheel supports data-driven R&D strategies by helping teams aggregate imaging data, streamline workflows, collaborate efficiently, and develop analysis-ready datasets for research and AI development.
• Clinical Researchers: Beyond functioning as a data repository, Flywheel serves as a scalable medical image management system and research workflow solution, enabling secure access, processing, analysis, and collaboration on imaging data.
• Medical Device Developers: For companies seeking to meet R&D objectives and accelerate regulatory submissions, Flywheel provides a scalable, cloud-based medical image analysis platform capable of ingesting data from multiple sources and unlocking it for device development.
• Academic Medical Centers and AI Developers: The platform supports machine learning workflows, model training, and algorithm development, making it a versatile tool for a broad range of healthcare innovation use cases.

Core Platform Capabilities

• Cohort Discovery: Tools to identify and assemble relevant patient or subject cohorts from large imaging datasets.
• Dataset Curation: Automated processes for organizing, cleaning, and preparing imaging data for downstream research and AI use.
• Project Validation: Capabilities to validate datasets and workflows to ensure research reproducibility and data integrity.
• Complex Analysis: Support for advanced imaging analysis workflows, enabling researchers to derive meaningful insights from their data.
• Algorithm Exchange and Model Training: Infrastructure for sharing algorithms and training AI/machine learning models, facilitating collaboration between medical researchers, data scientists, and stakeholders.
• Automated Research Workflows: End-to-end automation of data aggregation, curation, and processing pipelines to reduce manual effort and accelerate research timelines.

Key Products and Platform Evolution

• Flywheel Core: Launched in 2023, Flywheel Core is a software-as-a-service (SaaS) solution that provides an end-to-end AI development experience within a single platform. It accelerates model development by freeing up data preparation time, enabling reproducibility of AI models, and facilitating collaboration across teams.
• Radiologics Acquisition (2021): Flywheel expanded its capabilities through the acquisition of Radiologics, creating a unified imaging data and AI platform that further accelerates research and machine learning development for healthcare innovation.
• Clinical Trial and Medical Device Market Products: In 2025, Flywheel launched dedicated products to support clinical trial management and medical device development, reflecting the platform's evolution from a core lab research tool to a full enterprise imaging management solution.

Notable Customers and Success Stories

• Stanford Psychiatry and Behavioral Sciences: Flywheel powers automated workflows in Stanford's Brain Stimulation Lab, supporting a life-changing therapeutic for treatment-resistant depression known as Stanford Neuromodulation Therapy. The team uses the platform to process large volumes of patient data in pre- and post-treatment workflows and to develop automated tools for identifying personalized brain stimulation targets.
• University of California, San Francisco: A ten-year, 19-site study on traumatic brain injury (TBI) relies on Flywheel to aggregate and securely share medical imaging and related data across multiple research sites, overcoming the significant challenges of sharing large image files.
• Octave Bioscience: A data scientist at Octave Bioscience has leveraged Flywheel's openness and extensibility to accelerate AI development work in care management for multiple sclerosis, including custom viewers, third-party integrations, and partner collaborations.

Deployment, Support, and Funding

• Flywheel is delivered as a cloud-based, scalable SaaS platform requiring minimal IT investment, making it accessible to organizations of varying sizes and technical capabilities.
• The platform supports key partner integrations that extend its capabilities and enable a comprehensive end-to-end AI development experience.
• Flywheel's Scientific Solutions Engineers are professionals with real-world research experience who consult with and train customer teams to help them optimize their research workflows.
• In early 2026, Flywheel announced the close of an oversubscribed $25 million financing round led by Novalis Lifesciences and 8VC, alongside record growth and the launch of new products targeting clinical trial and medical device markets.
• The company is headquartered in Minneapolis, MN, and serves a global customer base spanning life sciences, healthcare, and academia.

As healthcare continues to evolve toward more data-driven and AI-assisted care, Flywheel is positioned to play an increasingly important role in accelerating the discoveries that are transforming how healthcare is delivered to patients around the world.