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As AI transforms enterprise operations across diverse industries, critical challenges continue to surface around data storage—no matter how advanced the model, its performance hinges on the ability to access vast amounts of data quickly, securely, and reliably. Without the right data storage infrastructure, even the most powerful AI systems can be brought to a crawl by slow, fragmented, or inefficient data pipelines.
This topic took center stage on Day One of VB Transform, in a session focused on medical imaging AI innovations spearheaded by PEAK:AIO and Solidigm. Together, alongside the Medical Open Network for AI (MONAI) project—an open-source framework for developing and deploying medical imaging AI—they are redefining how data infrastructure supports real-time inference and training in hospitals, from enhancing diagnostics to powering advanced research and operational use cases.
Innovating storage at the edge of clinical AI
Moderated by Michael Stewart, managing partner at M12 (Microsoft’s venture fund), the session featured insights from Roger Cummings, CEO of PEAK:AIO, and Greg Matson, head of products and marketing at Solidigm. The conversation explored how next-generation, high-capacity storage architectures are opening new doors for medical AI by delivering the speed, security and scalability needed to handle massive datasets in clinical environments.
Crucially, both companies have been deeply involved with MONAI since its early days. Developed in collaboration with King’s College London and others, MONAI is purpose-built to develop and deploy AI models in medical imaging. The open-source framework’s toolset—tailored to the unique demands of healthcare—includes libraries and tools for DICOM support, 3D image processing, and model pre-training, enabling researchers and clinicians to build high-performance models for tasks like tumor segmentation and organ classification.
A crucial design goal of MONAI was to support on-premises deployment, allowing hospitals to maintain full control over sensitive patient data while leveraging standard GPU servers for training and inference. This ties the framework’s performance closely to the data infrastructure beneath it, requiring fast, scalable storage systems to fully support the demands of real-time clinical AI. This is where Solidigm and PEAK:AIO come into play: Solidigm brings high-density flash storage to the table, while PEAK:AIO specializes in storage systems purpose-built for AI workloads.
“We were very fortunate to be working early on with King’s College in London and Professor Sebastien Orslund to develop MONAI,” Cummings explained. “Working with Orslund, we developed the underlying infrastructure that allows researchers, doctors, and biologists in the life sciences to build on top of this framework very quickly.”
Meeting dual storage demands in healthcare AI
Matson pointed out that he’s seeing a clear bifurcation in storage hardware, with different solutions optimized for specific stages of the AI data pipeline. For use cases like MONAI, similar edge AI deployments—as well as scenarios involving the feeding of training clusters—ultra-high-capacity solid-state storage plays a critical role, as these environments are often space and power-constrained, yet require local access to massive datasets.
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