Practical perspectives on data migration, interoperability, governance, de-identification, and AI readiness — written for healthcare organizations managing complex data environments.
The structural barriers that separate a great healthcare AI product from a scalable business — partnership friction, the funding gap, and the burden of continuous proof — and what it will take to close that gap.
Read the Article →A practical guide for healthcare IT teams on migrating imaging archives between PACS and VNA systems while maintaining HIPAA compliance, chain-of-custody documentation, and verified data integrity throughout the migration lifecycle.
Read Article →A regularly updated reference guide covering health-specific privacy requirements across all 50 states — including consent obligations, de-identification standards, enforcement actions, and implications for healthcare AI data governance programs.
Read Article →New eye-tracking research reveals that AI decision support doesn't just influence what radiologists conclude — it physically changes where they look, how long they dwell, and which regions they never examine at all. The mechanism of influence operates below the level of conscious awareness.
Read Article →U.S. law permits deleting radiology studies after 5–10 years. AI can now retroactively read those studies and find what was missed at the time of the original read. Jim Cook on the legal framework, the clinical implications, and what imaging organizations need to understand about long-term archive decisions in an AI-enabled environment.
Read Article →The AI boom demands serious physical infrastructure — in energy, compute, and healthcare data pipelines. Why responsible AI growth starts with building the right data foundation before deployment.
Read Article →A comprehensive guide covering HIPAA fundamentals, both de-identification methods, patient consent requirements, state law variations, AI-specific considerations, risk management, contractual protections, and recent regulatory developments — including the 18 PHI identifiers, re-identification risks, and DICOM burnt-in PHI.
Read Article →Answers to the most common questions about our clinical data infrastructure model: why we monetize health data, who controls it, how de-identification works, why no PHI ever leaves the facility, and how our platform benefits small and mid-sized hospitals and their communities.
Read Article →AI capability is increasingly constrained by grid reality. Jim Cook examines the electron gap — the widening mismatch between AI ambition and the electrical infrastructure required to run AI at scale — and what it means for leaders trying to move AI from pilot to production.
Read Article →Palantir's CTO called AI America's potential slingshot against China. He's right — but the advantage will be won or lost at the infrastructure layer, not the algorithm layer. Jim Cook examines the two AI models competing for dominance and what it means for deployment at scale.
Read Article →Jim Cook on why trust in healthcare AI is fundamentally a data integrity and governance problem — and what healthcare organizations need to do operationally before they deploy AI.
Read Article →As we approach 1,000 FDA-cleared AI tools in medical imaging, the question is no longer 'Does AI work?' — it's 'Who is responsible when it doesn't?' Jim Cook examines the governance gap threatening to undermine AI's promise in healthcare imaging, drawing on two decades of DICOM experience.
Read Article →Yoshua Bengio's TED warning about agentic, opaque AI maps cleanly onto radiology, cardiology, and pathology. Jim Cook on automation bias, hallucinations, population-level inequity, and what a safer path looks like for clinical AI.
Read Article →We're adding new content regularly. Check back soon, or suggest a topic.
Founder & CEO, Health AI Data, Inc. · President, CEO & CAITO, Radiant AI Health Data, Inc. · Senior PACS Administrator
Jim Cook brings more than 28 years of healthcare IT experience, including over two decades of direct operational work with DICOM systems, enterprise PACS, and clinical imaging infrastructure. He currently serves as Clinical Application Support Specialist IV at Solis Mammography, giving him day-to-day visibility into the imaging environments that clinical AI is being deployed on top of. His writing sits at the intersection of practitioner experience, executive perspective, and AI governance — focused on what healthcare organizations actually need before, during, and after AI deployment.
Healthcare AI is being discussed everywhere. What's being discussed far less is whether the underlying data infrastructure can actually support it. PACS systems that haven't been modernized, data that hasn't been governed or de-identified, and imaging environments with no interoperability layer cannot serve as the foundation for clinical or research AI — regardless of how advanced the model is.
Radiant Insights exists to have that honest conversation. We write for the people inside healthcare organizations who are responsible for making infrastructure decisions — not just the ones evaluating AI demos.
Written by a Senior PACS Administrator with direct operational experience in enterprise imaging and healthcare data environments — not marketing copy.
We focus on the foundational layer — migration, interoperability, governance, and de-identification — because that's where AI readiness actually begins.
Healthcare data modernization is difficult. We don't simplify it. We explain what it actually takes, so organizations can plan and invest accordingly.
We're not here to sell a demo. We're here to help healthcare organizations make better decisions about their data infrastructure — now and for the long term.
Radiant Insights covers the full lifecycle of healthcare data infrastructure modernization.
De-identification, policy frameworks, and compliant data handling for clinical and research use.
Modernization planning, infrastructure investment, and long-term data program development.
HIPAA de-identification, state privacy laws, contractual protections, and regulatory guidance.
PACS replacement, VNA migration, archive consolidation, and enterprise transitions.
Building the data infrastructure layer that clinical and research AI actually requires.
Browse the complete Radiant Insights library across all topics and resource types.
Radiant AI Health Data is built for organizations that need a stronger foundation for migration, interoperability, governance, and AI readiness. We'd welcome the conversation.