Flywheel Archives - Medical Design Sourcing

Medical imaging AI startup Flywheel wins Series D round backed by NVIDIA, Microsoft and HPE

[Image courtesy of Flywheel]

Minneapolis-based Flywheel has received $54 million in a Series D round backed by NVIDIA, Microsoft, Hewlett Packard Enterprise and other strategic investors.

Flywheel will invest the cash to fuel growth in core verticals, the pharmaceutical and public sector healthcare sectors. A notable Big Pharma customer is Genentech. The company also aims to ramp up expansion into emerging areas such as healthcare providers, payers, IT service providers and software vendors. Finally, Flywheel intends to extend its global footprint, especially in key markets in Europe.

Current challenges in medical AI

Medical imaging has long faced challenges with data complexity and a mosaic of metadata and formats, including DICOM (Digital Imaging and Communications in Medicine) to JPG or TIFF and modalities spanning MRI, CT, PET and ultrasound. Despite some standardization, fragmented formats prevail fr…

7 imaging data management strategies to accelerate medical device innovation

[Image courtesy of Flywheel]

Medical device companies have massive amounts of data, but often, imaging data isn’t set to support R&D efforts. Here’s how to change that.

Jim Olson, Flywheel

Data is the lifeblood of R&D groups at medical device companies. However, unlocking the full potential of your organization’s data assets isn’t straightforward, especially for complex data such as medical imaging.

Medical imaging assets are extremely valuable for R&D efforts, but they are often disorganized and lack consistent labeling. This means that before they can be used for analysis and/or for machine learning or AI applications, they must be standardized and made accessible.

But curating complex medical imaging data poses a major challenge in many organizations. Even after data are organized, existing infrastructure and research processes can continue impeding success, and ultimately time …

Fueling breakthroughs in pharma AI: 3 critical factors

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Big data and AI offer massive opportunities to the pharmaceutical industry — in theory. In reality, many companies are struggling to realize the potential of these tools. Some organizations have been hesitant or resistant to leveraging the technologies. Others may have attempted to embrace them early on but are now beginning their second or third incarnations of “digital transformation,” likely with some layoffs along the way.

Why the difficulty? Digital transformation is, of course, a massive undertaking — requiring enterprise-wide coordination and a clear, focused vision. In the real world, organizations have struggled with defining a focus for their AI efforts and sustaining the investments necessary to reach them. It’s easy to get excited about the prospect of using AI to solve everything under the sun, but more often, successes are coming when teams stay focus…

5 common data management problems affecting drug discovery

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Ask a pharma researcher how well they’re able to leverage their organization’s medical imaging data, and you might hear a discouraging response. While most pharma companies have massive amounts of clinical and medical imaging data, often, most of the imaging data isn’t ready for modern research processes and infrastructure. This imaging data is an untapped asset — it’s disorganized, difficult or impossible to query, not normalized and in no way ready for machine learning and AI. The result is innovation is slowed.

Imaging data is a rich source of information that can hold the key to many discoveries, but it is complex to work with. Pharma companies need a sophisticated data management infrastructure to help manage this complexity and seek to scale up their research.

Here are five common data management problems to consider as your organization evaluates its path forward.

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