Imaging Informatics Unplugged

Nagels Consulting

Welcome to ‘Imaging Informatics Unplugged,’ the podcast where host Jason Nagels delves into the dynamic world of medical imaging informatics. From interoperability standards like DICOM, HL7, and FHIR to the latest AI innovations and enterprise imaging, Jason brings you insightful discussions and expert interviews illuminating the path toward seamless healthcare technology integration. Whether you’re a seasoned professional or new to the field, join us as we explore the technologies and trends shaping the future of medical imaging. nagelsconsulting.com / learn.nagelsconsulting.com

  1. -4 дн.

    Task Shifting & Radiology IT: How Nunavut Closed Its Imaging Access Gap | Greg Toffner

    What happens when a remote Arctic community with zero medical radiation technologists needs a chest x-ray — and the nearest one is a plane ride away? In this special crossover episode with SIIMCast, Jason and Mohannad sit down with Greg Toffner, a PhD researcher training local Inuit community members across 25 Nunavut communities to become Basic Radiological Technicians (BRTs). Greg breaks down the concept of task shifting, how it’s reshaping radiology IT access in one of the most geographically isolated regions on earth, and what six years of hands-on curriculum design, mobile x-ray equipment, and government policy work have taught him about building a sustainable imaging informatics workforce from the ground up.You’ll hear real numbers on health disparities in the North, how a 60-procedure competency framework turns a health center janitor into a trusted member of the care team, and where AI might eventually fit into enterprise imaging and radiology workflows in low-resource settings. It’s a conversation about access, dignity, and what imaging informatics looks like when PACS, DICOM, and specialist radiologists aren’t around the corner.If you’re building your own credential in imaging informatics, check out the CIIP Foundations Program at nagelsconsulting.com, and keep an eye out for our upcoming DICOM training program featuring hands-on, live imaging learning labs: Learn more at nagelsconsulting.comKey Topics Covered• What “task shifting” means and how it’s applied to solve radiology workforce shortages in remote Arctic communities• Life inside Nunavut’s 25 flying communities — the geography, climate, and health disparities driving the program (life expectancy, respiratory disease, and smoking rates)• How Greg’s team built a simplified radiology curriculum for learners with no formal healthcare background• The three-phase, hands-on training model — from image critique to a 60-procedure competency audit• Why government policy recognition, not just curriculum quality, is what makes a task-shifted role sustainable• Early findings from Greg’s PhD research: pride, community trust, and the ripple effects on local health outcomes• Where the program is headed next — expanded scope into lab work and ECGs, and where AI might fit inhealthcare IT, medical imaging, radiology technology, healthcare interoperability, PACS administration, DICOM standard, HL7 integration, radiology informatics, imaging workflows, vendor neutral archive, enterprise imaging, radiology AI, medical imaging data, PACS migration, rural health access

    47 мин.

  2. 25 июн.

    AI Coding, DICOM Sync & the Future of PACS | Chris Hafey

    What happens when a 25-year medical imaging veteran retires, then comes back with an AI coding assistant and rebuilds his entire mental model of how PACS gets built? Chris Hafey, founder of Merkalis and longtime fixture in the imaging informatics community, joins Imaging Informatics Unplugged to talk about coding with Claude and other LLMs, why he believes solo founders can now out-build 50-person dev teams, and what that means for PACS administration, Radiology IT, and Enterprise Imaging going forward. We dig into the real story behind his Monday-morning community meetups, the skill-degradation risk facing junior engineers as AI takes over more of the coding, and the Merkle-tree-based architecture Chris built to solve DICOM synchronization — a problem that's plagued Vendor Neutral Archives and Imaging Informatics teams trying to keep on-prem and cloud PACS in sync. Chris also explains why he thinks some legacy PACS vendors would be better off rebuilding from scratch than fighting decades of technical debt, and what AI in Radiology and HL7-connected systems need to actually trust the data they're syncing. If you work anywhere near PACS, DICOM, or healthcare IT and want an unfiltered, practical conversation instead of a vendor pitch, this one's for you.Jason also shares an update on the CIIP Foundations Program for imaging informatics professionals working toward their credential, plus a first look at the new DICOM training program featuring hands-on live imaging learning labs. Learn more at nagelsconsulting.com.Key Topics Covered• How Chris's Monday-morning imaging community meetup started during COVID, went on hiatus, and came back unfiltered• Going from hand-coding for a month to building a full DICOM web server with OHIF integration in about two hours of real thinking time using Claude• Why Chris believes a small domain-expert team using LLMs can now do what used to require a 50-person dev team and tens of millions in funding• The skill-degradation risk for junior and senior engineers as LLMs take over more of the actual coding• Why some legacy PACS vendors might be better off rebuilding from scratch than maintaining decades of technical debt• Applying a Git-like delta model to DICOM so PACS, VNAs, and AI pipelines can stay in sync without re-sending whole studiesTags• healthcare IT• medical imaging• radiology technology• healthcare interoperability• PACS administration• DICOM standard• HL7 integration• radiology informatics• imaging workflows• vendor neutral archive• enterprise imaging• radiology AI• AI coding assistants• PACS migration• healthcare data management

    41 мин.

  3. 20 июн.

    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 Covered The 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

    42 мин.

  4. 4 июн.

    Digital Pathology Display Standards: Why Medical Monitors Change Diagnostic Accuracy | Tom Kimpe

    If your digital pathology deployment has a brand-new whole-slide scanner, a solid IMS, and petabytes of storage — but your pathologists are reading on consumer monitors — you may have a serious problem you haven't budgeted for yet.In this episode of Imaging Informatics Unplugged, Jason sits down with Tom Kimpe, VP of Technology & Innovation for Healthcare at Barco, for a deep-dive webinar from Canada Health Infoway on the critical role the display plays in the digital pathology imaging chain. Tom unpacks why color gamut matters more in pathology than almost any other imaging domain, how color variability sneaks in at every step of the workflow — from tissue staining through scanner to viewer to display — and what the peer-reviewed science actually says about diagnostic accuracy and reading efficiency when pathologists use medical-grade versus consumer monitors.Spoiler: a 6–8% reduction in reading time. 100% diagnostic concordance on medical displays versus measurable drop-off on consumer hardware. Missed concurrent diseases. These aren't marketing claims — they're published, peer-reviewed findings.Whether you're a PACS admin, imaging informatics specialist, or radiology IT leader expanding into enterprise imaging and digital pathology, this is the kind of workflow and display standardization knowledge that will save your organization from an expensive mid-project scramble. If you're working toward your CIIP credential, the CIIP Foundations Program at nagelsconsulting.com is exactly where this conversation fits into the bigger picture — and keep an eye out for the upcoming DICOM training program with hands-on live imaging learning labs that will make concepts like ICC profiles and DICOM WG-26 click in a whole new way.Learn more at nagelsconsulting.comKey Topics Covered Why pathology tissue has a broader colour gamut than sRGB displays can reproduce — and what that means clinicallyHow colour variability is introduced at every link in the digital pathology chain: lab prep, scanner, viewer, and displayThe difference between consumer, professional, and medical-grade displays — and why it matters for pathology specificallyICC colour profiles: what they are, how they work, and why adoption is accelerating toward an industry standardPeer-reviewed evidence showing measurable impact of display quality on diagnostic accuracy, concordance, and reading efficiencyThe DICOM WG-26 ecosystem: how file format standardization is finally catching up to scanner adoptionWhere display procurement falls through the cracks in digital pathology deployments — and how to fix it

    45 мин.

  5. 3 июн.

    AI in Radiology: Benchmarking LLMs, Agentic Hype, and Imaging Informatics | Satvik Tripathi

    If you have ever watched a radiology AI demo hit 98% accuracy in testing and then wonder why nobody is actually using it in the clinic, this episode is for you. Hit subscribe so you never miss a conversation like this one.Jason sits down with Satvik Tripathi, incoming Medical Physics and Imaging Informatics PhD student at the University of Pennsylvania, AI scientist for RAD-AID International, and one of the sharpest voices in the field on the gap between research performance and real clinical value. Satvik has been working at the intersection of AI and radiology since 2019 and brings a perspective that cuts through the noise.They get into the hard questions: why multiple-choice benchmarks are a terrible way to evaluate medical LLMs, what data leakage is quietly doing to published performance numbers, and why a fine-tuned model is not always the winner in a clinical context. Satvik also breaks down what it actually takes to build a benchmark that means something, and shares early findings from his team's head-to-head testing of over 20 models on an internally annotated clinical dataset.The conversation also digs into agentic AI in imaging informatics, global health deployments through RAD-AID in Botswana and India, AI-assisted oncology workflows, and why running smaller open-source models locally might be smarter than everyone thinks. Plus, Satvik makes a case that prompt engineering is not a productivity shortcut but a legitimate scientific method.Whether you are a PACS administrator, imaging analyst, radiology IT professional, or just someone trying to figure out which AI tools are actually worth your time, this is the kind of conversation that helps you cut through the hype and think more clearly about what is coming.If this episode is useful to you, please subscribe, leave a review, and share it with a colleague in the imaging informatics community. It makes a real difference. And if you are working toward your CIIP credential or want to go deeper on the foundations of this field, 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 Covered Why AI model performance metrics often fail to predict real-world clinical impact, and the two questions every AI deployment team should be asking before going liveThe flaws in how medical LLMs are benchmarked today, including multiple-choice test limitations, data leakage, and the gap between controlled evaluations and actual clinical usefulnessHow Satvik's team built an internal annotated dataset and tested more than 20 models head-to-head, with results that challenge conventional assumptions about fine-tuned modelsThe promise and current limitations of agentic AI in radiology, including what true agentic systems require versus what vendors are actually shipping• Using AI to democratize global healthcare through RAD-AID's work in Botswana and India, including Google-funded foundation model deployments and lessons that translate back to Western healthcare systemsWhy prompt engineering is a scientific method, not just a productivity trick, and how structured prompting can reduce hallucinations and improve reproducibility in clinical AI applicationsThe practical case for smaller, on-premises open-source models over large cloud-based generalist models, including cost, privacy, sustainability, and compliance considerations

    36 мин.

  6. 20 апр.

    CIIP Exam Prep: Where to Start, What to Study, and How to Qualify

    Thinking about the CIIP, but have no clue where to start?This video breaks down the exam, the eligibility rules, the 10 domains, and the smartest way to figure out what you actually need to study before wasting time. If you work in imaging informatics, PACS, VNA, radiology IT, enterprise imaging, clinical engineering, or healthcare technology, this gives you a practical starting point.In this video, you’ll learn:• what the CIIP actually signals to employers• how the exam is structured• what the 10 test content outline domains are• how the questions are weighted across domains• what the ABII seven-point qualification system is• how to check whether you qualify before diving in• how a free 50-question practice quiz can help you find strengths, spot gaps, and focus your prepThe CIIP is not just for “technical people.” It sits at the intersection of healthcare, operations, systems, workflow, and imaging technology. That’s what makes it valuable, and that’s also why a lot of people feel overwhelmed when they first look at it. This video is meant to make that starting point clearer.I also introduce the CIIP Foundations Program from Nagels Consulting, a structured prep path aligned to the same 10 domains covered on the exam. The goal is simple: help you study with direction rather than guess.Start here:Free 50-Question CIIP Practice Quiz: https://learn.nagelsconsulting.com/course/ciip-practice-tools ABII Eligibility Guide: https://www.abii.org/Qualification-Requirements.aspx Learn more: www.nagelsconsulting.com First five CIIP Foundations courses:CIIP_PROC101: Procurement as a Repeatable Thinking Processhttps://learn.nagelsconsulting.com/course/ciip-proc101 CIIP_PM101: Project Management as a CIIP Core Competencyhttps://learn.nagelsconsulting.com/course/ciip-pm101 CIIP_OPS101: CIIP Operations & System Reliabilityhttps://learn.nagelsconsulting.com/course/ciip-ops101 CIIP_COM101: Communication as a Cross-Domain Disciplinehttps://learn.nagelsconsulting.com/course/ciip-com101 CIIP_EDU101: Training & Education in Imaging Operationshttps://learn.nagelsconsulting.com/course/ciip-edu101 #CIIP #CIIPExam #CIIPCertification #ImagingInformatics #EnterpriseImaging #PACS #VNA #ImagingIT #HealthcareIT #ClinicalEngineering #MedicalImaging #ABII #ExamPrep #NagelsConsulting

    7 мин.

  7. 16 апр.

    AI in Radiology: Adoption, Bias, Interoperability, Federated Learning and What Comes Next

    How close is AI to real clinical adoption in radiology and medical imaging?In this episode of Imaging Informatics Unplugged, Jason Nagels talks with Dr. Khaled Younis about where AI and machine learning really stand today in radiology, why adoption is still uneven, and what needs to happen next.Dr. Younis brings deep experience across AI research, clinical imaging, and global standards. You can learn more about his work here:https://medaiconsult.com/https://www.linkedin.com/in/dryounis/The conversation digs into the biggest barriers holding imaging AI back, including clinical validation, FDA and regulatory scrutiny, interoperability challenges across PACS, RIS, EHR, DICOM, HL7 and FHIR, as well as bias, trust, explainability and real-world deployment. It also looks at how early CAD systems compare with today’s deep learning era, why many AI vendors still treat standards as an afterthought, and how IHE and ISO are shaping the future of trustworthy AI in imaging.You’ll also hear a practical discussion on federated learning, multi-site collaboration, synthetic data, tumor segmentation, structured AI results, and the role of standards in making AI outputs usable across clinical workflows. Dr. Younis shares where he sees the biggest opportunities ahead, including real-time decision support, AI-assisted intervention, multimodal data integration, and more open, interoperable healthcare ecosystems.If you’re looking to build a stronger foundation in imaging informatics, workflows, and standards like DICOM, HL7, FHIR, and IHE, visit:https://www.nagelsconsulting.com/You can also check out the CIIP Foundations program for a structured, practical approach to understanding how these concepts apply in real-world imaging environments.Topics coveredAI in radiology adoptionMedical imaging AI barriersFDA approval and post-market surveillanceInteroperability in radiology AIIHE profiles and AI resultsISO and trustworthy AIBias in healthcare AIFederated learning in medical imagingSynthetic data for AI trainingStructured reporting and TID 1500Real-time decision supportMultimodal AI in healthcareHashtags#AI #Radiology #MedicalImaging #ImagingInformatics #HealthcareAI #MachineLearning #FederatedLearning #DICOM #IHE #FHIR #HL7 #ClinicalAI #TrustworthyAI #Interoperability #DigitalHealth

    48 мин.

  8. 11 мар.

    AI in Radiology: Hype vs Reality, Imaging AI, Workflow, and Clinical Adoption | Dr. Ben Fine

    Is radiology AI finally living up to the hype — or are we still waiting for the revolution Geoff Hinton promised back in 2012? In this episode of Imaging Informatics Unplugged, Jason sits down with Dr. Ben Fine, a radiologist with a deep background in imaging informatics, AI deployment, and enterprise imaging strategy. Ben shares a refreshingly honest take on where radiology AI actually stands today: only about 1% of imaging workflows are meaningfully augmented by AI tools, and real ROI is still rare — driven more by FOMO than outcomes. But he argues the corner is being turned, as the field shifts from narrow deep learning models to foundation models capable of assisting with full radiology reports. The conversation digs into real-world AI deployment lessons from the AIDE Lab at Trillium Health Partners, where Ben’s team developed a pre-deployment evaluation methodology for PACS-integrated AI tools that has since become best practice. They also explore the Swiss cheese model of human-AI collaboration, why operational workflow use cases (like protocol automation and procedure nomenclature mapping) often beat flashy diagnostic AI for ROI, and what effective AI governance looks like for health systems. Whether you’re a PACS Admin, Imaging Manager, Radiologist, or Healthcare IT professional thinking about enterprise imaging and AI in radiology, this episode is packed with practical, colleague-to-colleague insights. Learn more at nagelsconsulting.com KEY TOPICS COVERED Amara’s Law and the realistic 3-year outlook for radiology AI — why we’re finally at the inflection point after years of overhypeThe AIDE Lab at Trillium Health: how pre-deployment evaluation of PACS-integrated AI tools became a best-practice framework across OntarioThe Swiss cheese model of human-AI collaboration — why AI and radiologists fail in such different ways, and how to design systems that catch what neither misses aloneOperational AI use cases that deliver real ROI: procedure nomenclature mapping, CT/MRI protocol automation, and bone mineral density (BMD) workflow assistance• Ontario’s centralized diagnostic imaging repository (OCINet/DIR) as an untapped opportunity for population health AI and opportunistic screening at scaleAI governance frameworks for health systems — applying a pharmaceutical-committee model to the selection, validation, deployment, and monitoring of AI toolsThe future skills that matter: why domain expertise combined with AI fluency — not just soft skills — will define the next generation of imaging informatics professionals

    43 мин.
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Welcome to ‘Imaging Informatics Unplugged,’ the podcast where host Jason Nagels delves into the dynamic world of medical imaging informatics. From interoperability standards like DICOM, HL7, and FHIR to the latest AI innovations and enterprise imaging, Jason brings you insightful discussions and expert interviews illuminating the path toward seamless healthcare technology integration. Whether you’re a seasoned professional or new to the field, join us as we explore the technologies and trends shaping the future of medical imaging. nagelsconsulting.com / learn.nagelsconsulting.com

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