Why VNAs Were Never Really About Storage — And What That Means for AI | Larry Sitka

If you've ever fought through a PACS migration, wrestled with malformed DICOM data, or wondered why two systems that supposedly speak the same standard can't talk to each other — this one's for you.Jason sits down with Larry Sitka, founder of Acuo Technologies and one of the earliest architects of the Vendor Neutral Archive (VNA) concept, to unpack three decades of building enterprise imaging infrastructure from the ground up. Larry got his start writing network drivers at Bell Labs, helped shape DICOM 3.0 at 3M, and then built Acuo — a company he grew from a sketch on a napkin in 1997 into an enterprise imaging powerhouse acquired twice over.In this episode, Larry and Jason dig into why DICOM interoperability is still a mess, how AI false positives are driving radiologist frustration, and why the next evolution of enterprise imaging isn't about storing data — it's about perceiving it. They also tackle the knowledge gap that's forming as experienced imaging IT professionals retire, what AI governance for radiology actually looks like, and why Larry thinks the industry has been building things for the wrong user all along.Whether you're a PACS administrator, imaging informatics professional, or just someone who cares about getting radiology AI right, this conversation will give you a lot to chew on.If you're looking to build a stronger foundation in imaging informatics or sharpen your DICOM knowledge, check out the CIIP Foundations Program and the upcoming DICOM training with hands-on live imaging learning labs at nagelsconsulting.com.Learn more at nagelsconsulting.comKey Topics CoveredThe origin story of Acuo and why the VNA concept emerged in 1997 — before anyone had a name for itWhy DICOM interoperability remains broken and what a real conformance testing standard would look likeThe shift from data persistence to data perception — and how AI changes what we actually need from a VNAHow AI false positives are burning out radiologists and what multi-algorithm inference engines could do insteadThe knowledge gap in imaging IT: what gets lost when experienced DICOM engineers retireAI governance for enterprise imaging — why recalibrating AI models is the next big challenge in PACS/VNAThe future of enterprise imaging: running a million AI inferences a night at population scale