Imaging Informatics Unplugged

Επεισόδια

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  Enterprise Imaging Interoperability: DICOM, FHIR, PACS and VNA Data Sharing | Michael Rosenberg 20.08.2026 54λ

  Michael Rosenberg has spent the last decade obsessed with a problem most of healthcare IT would rather not think about: how do you get a medical image from one institution to another, safely and without a human babysitting every step? An industrial and systems engineer by training, Michael cofounded Medicom Technologies in 2015 out of North Carolina State University, and as CEO he and his team built the company around what they call Enterprise Imaging Interoperability. Their software now runs across every VA medical center in the country along with some of the nation's leading IDNs and academic medical centers. In this conversation, Jason sits down with Michael to dig into why some of the most well-intentioned efforts in image sharing, the ACR's Ditch the Disc campaign among them, never fully solved the problem, and what it will actually take to get there.They start by grading the ACR's Ditch the Disc initiative: strong on messaging, short on the policy and mandates needed to change workflow. From there, Michael explains why three-point patient matching (name, date of birth, and one other identifier) is still common in DICOM image exchange, and why he considers it unsafe. He walks through Medicom's FHIR-based approach for pulling additional demographics from the EHR before a match is made, and why the study instance UID deserves treatment as a universal identifier across systems.Jason and Michael also dig into what a national scale rollout looks like, drawing on Medicom's work across the VA network, and how that compares to centralized models already in place in Canada and parts of Europe. From there they cover patient-driven image sharing, including a surprisingly high adoption rate for patient upload tools, the privacy risks buried in supposedly de-identified imaging data, and what happens when interoperability moves beyond radiology into pathology, cardiology, and oncology, where the workflows barely resemble each other. They close with a look at where imaging regulation is headed, including proposed ONC certification requirements that could finally make interoperability mandatory rather than optional.If PACS, DICOM, HL7, VNA, or enterprise imaging AI are part of your world, this one's worth the full listen.Jason also has a quick update from nagelsconsulting.com: the Imaging Informatics Primer course is a great starting point if you're new to the field, the CIIP Foundations material is built for anyone prepping for certification, and the hands-on DICOM Learning Lab gives you live, practical reps with the standard itself.Learn more at nagelsconsulting.com#ImagingInformatics #PACS #DICOM #HealthcareIT #RadiologyIT #EnterpriseImaging #VNA #HL7 #RadiologyAI #MedicalImaging #Interoperability #PatientMatching #FHIR #HealthIT #DitchTheDisk5. Chapter List (Timestamps)Note: chapter times are offset by 1:23 to account for the intro script
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  ClearCanvas, Open-Source PACS, and the Future of Imaging Informatics | Norman Young 05.08.2026 38λ

  Norman Young has spent 25 years building software at the intersection of imaging and medicine, but the story behind his work starts with his own diagnosis. In 1992, as a third-year engineering student at the University of Toronto, Norman was diagnosed with Hodgkin's lymphoma, an experience that reshaped the rest of his career. After working on eFilm, one of the first free PACS viewers, at University Health Network, he founded ClearCanvas in 2005, an open-source medical imaging platform still used by clinicians around the world. In 2021, the American College of Radiology recognized his work with their Global Humanitarian Award. Today Norman is building something new: CareChorus, a nonprofit aimed at helping patients and caregivers make sense of their own medical records.Jason and co-host Mohannad Hussain sit down with Norman to trace that whole arc. He explains why ClearCanvas went beyond free software into full open source, what it took to build a professional-grade PACS and RIS largely on his own before landing the hospital contract that got the company off the ground, and what it was like watching download counts climb from every continent, including, improbably, Antarctica. They also get into how AI coding tools like Claude Code and Codex are changing what a small team can ship, and why Norman still insists on reviewing every architectural decision even when the AI writes the first draft.The back half turns personal. Norman walks through the two stories, one about a friend's husband facing a serious diagnosis, the other about a colleague who passed away after struggling to get coordinated care, that led him to found CareChorus. He breaks down the four problems the platform is built to solve, from organizing scattered medical records to helping patients actually understand what their reports mean, and explains why he built it as a nonprofit with data that stays on the user's own device rather than in the cloud.If PACS, DICOM, open-source imaging informatics, or healthcare IT are part of your world, this conversation is worth the full listen.Jason also has an update from nagelsconsulting.com: the Imaging Informatics Primer course is a great starting point if you're new to the field, the CIIP Foundations material is built for anyone prepping for certification, and the hands-on DICOM Learning Lab gives you live, practical reps with the standard itself.Learn more at nagelsconsulting.com#ImagingInformatics #PACS #DICOM #HealthcareIT #RadiologyIT #OpenSource #EnterpriseImaging #HealthIT #MedicalImaging #PatientAdvocacy #AIinHealthcare #CIIP #DigitalHealth #HealthTech
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  Task Shifting & Radiology IT: How Nunavut Closed Its Imaging Access Gap | Greg Toffner 03.07.2026 46λ

  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
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  AI Coding, DICOM Sync & the Future of PACS | Chris Hafey 25.06.2026 41λ

  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
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  Why VNAs Were Never Really About Storage — And What That Means for AI | Larry Sitka 20.06.2026 41λ

  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
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  Digital Pathology Display Standards: Why Medical Monitors Change Diagnostic Accuracy | Tom Kimpe 04.06.2026 45λ

  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 CoveredWhy 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
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  AI in Radiology: Benchmarking LLMs, Agentic Hype, and Imaging Informatics | Satvik Tripathi 03.06.2026 35λ

  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 CoveredWhy 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
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  CIIP Exam Prep: Where to Start, What to Study, and How to Qualify 20.04.2026 6λ

  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
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  AI in Radiology: Adoption, Bias, Interoperability, Federated Learning and What Comes Next 16.04.2026 48λ

  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
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  AI in Radiology: Hype vs Reality, Imaging AI, Workflow, and Clinical Adoption | Dr. Ben Fine 11.03.2026 42λ

  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.comKEY TOPICS COVEREDAmara’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
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  The Beatles, EMI, and the Birth of CT Scanning | Imaging Informatics Unplugged 09.03.2026 5λ

  What do The Beatles have to do with the invention of the CT scanner?At first glance… nothing.By the time CT scanning was introduced in the early 1970s, The Beatles had already broken up. But the connection between one of the most influential bands in history and one of the most important technologies in modern medicine runs through a company called EMI.In this episode, we explore the surprising story of how revenue generated by Beatles records helped sustain the research division at EMI where Godfrey Hounsfield developed the first computed tomography (CT) scanner.We also explore how CT imaging transformed diagnostic medicine and how the field of imaging informatics helps manage imaging safely today.Topics covered include:• The invention of CT scanning• The role of EMI and Godfrey Hounsfield• The first clinical CT brain scan in 1971• Ionizing radiation and medical imaging• Radiation safety principles like ALARA• The Cedars-Sinai CT dose incident• Standards such as DICOM Radiation Dose Structured Reports (RDSR)• Regional and national dose registriesMedical imaging has revolutionized healthcare, allowing clinicians to see inside the body with unprecedented clarity. But the story behind these technologies sometimes has unexpected connections to music, culture, and history.And in this case… it starts with The Beatles.⸻Learn more about imaging informatics and enterprise imaging: https://www.nagelsconsulting.com⸻#imaginginformatics #medicalimaging #CTscan #radiology #healthcaretechnology #DICOM #ALARA #patientSafety #diagnosticImaging #healthIT
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  AI in Radiology Explained: From Pilot Projects to Real Clinical Deployment | Dr. Franz Pfister 25.02.2026 33λ

  AI in healthcare is everywhere — but very little of it actually works in real clinical environments.In this episode of Imaging Informatics Unplugged, Jason Nagels sits down with Dr. Franz Pfister, physician, data scientist, and CEO of deepc, to discuss what it really takes to operationalize AI in radiology and healthcare workflows.They explore why model accuracy alone isn’t enough, how hospitals deploy AI safely at scale, and what separates real operational AI from marketing hype.If you work in enterprise imaging, radiology, PACS, VNA, healthcare IT, or clinical AI, this conversation is essential⸻What You’ll LearnWhy most healthcare AI pilots never become operationalThe difference between model performance and workflow integrationHow AI is deployed safely inside clinical environmentsPrivacy, governance, and regulatory challenges (GDPR, AI Act)Performance drift and real-world AI reliabilityAgentic AI in healthcare: hype vs realityHow AI will reshape roles in radiology and imaging informatics• Treating AI as infrastructure, not just a tool⸻About the GuestDr. Franz MJ Pfister is a medical doctor, data scientist, and entrepreneur working at the intersection of AI, healthcare, and clinical operations. He studied medicine at Ludwig Maximilian University of Munich and Harvard Medical School, holds a doctorate in neuroscience, an MBA, and a Master’s in Data Science. He is CEO and co-founder of deepc, a company focused on operational AI infrastructure for radiology.⸻About Imaging Informatics UnpluggedImaging Informatics Unplugged explores enterprise imaging strategy, interoperability, PACS, VNA, clinical workflows, healthcare AI, and the future of medical imaging technology.Hosted by Jason Nagels of Nagels Consulting.⸻Learn More Website: https://www.nagelsconsulting.comBook a Meeting with Jason https://meetings.hubspot.com/jason-nagelsWatch on YouTube: https://www.youtube.com/@NagelsConsulting⸻Courses & Training in Imaging Informatics Nagels Consulting provides professional training in:Enterprise ImagingImaging InformaticsPACS & VNA StrategyInteroperability Standards (DICOM, HL7, FHIR)Healthcare AI ImplementationClinical Workflow OptimizationNew courses launching soon → https://www.nagelsconsulting.com
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  Dermatology Imaging Informatics & AI: Data, Context and Clinical Reality | Dr. Veronica Rotemberg 12.02.2026 28λ

  Artificial intelligence is rapidly entering dermatology — but what does it actually take to make AI work in real clinical practice?In this episode of Imaging Informatics Unplugged, Jason Nagels sits down with Dr. Veronica Rotemberg, Director of Dermatology Informatics and Research at Memorial Sloan Kettering Cancer Center, to explore how dermatology is navigating AI, data quality, and real-world implementation challenges.Dermatology is one of the most image-driven specialties in medicine, yet its imaging workflows have evolved very differently from those in radiology and pathology. As AI models promise improved diagnostic performance, new questions emerge around benchmarking, overfitting, clinical context, and bias.In this conversation, we cover:• Why AI became a forcing function for dermatology informatics• The limits of single-image benchmarking and reader studies• The “ugly duckling” concept in melanoma detection• Overfitting risks from lighting, markers, camera differences, and workflow artifacts• Skin tone bias in AI models and why labelling is harder than it sounds• Why testing AI in its intended clinical use setting is critical⸻⏱ Chapters00:00 – Introduction: Dermatology & Imaging Informatics02:33 – Dr. Rotemberg’s Journey into Dermatology Informatics05:26 – The AI Inflection Point (2017) and the Data Wake-Up Call07:58 – The “Wild West” of Dermatology Image Capture09:42 – AI Hype vs. Clinical Reality11:17 – Where AI Actually Fits in Dermatology Today12:45 – Single-Image Benchmarking and Reader Studies15:23 – The “Ugly Duckling” Concept Explained17:58 – Overfitting: Lighting, Cameras, and Workflow Artifacts18:42 – Augmentation Strategies and Hidden Bias Signals21:22 – Skin Tone Bias in Dermatology AI24:47 – Testing AI in the Intended Clinical Use Setting26:09 – The Future of Dermatology Informatics27:38 – “You’re an Informaticist. You Just Don’t Know It Yet.”⸻Dr. Rotemberg holds a PhD in Biomedical Engineering, leads an NIH-funded AI and informatics research lab, chairs the Augmented Intelligence Committee of the American Academy of Dermatology, and serves on the Board of Directors of the Society for Imaging Informatics in Medicine.If you’re interested in enterprise imaging, clinical AI validation, dermatology informatics, or bias in machine learning, this episode delivers a grounded, clinician-informed perspective.🔔 Subscribe for more deep conversations on imaging informatics, AI in healthcare, enterprise imaging, DICOM, FHIR, and real-world clinical systems.🌐 Keep an eye on https://nagelsconsulting.com for upcoming course launches and major updates to our imaging informatics training programs.
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  Inside the DICOM Toolkit: How Medical Imaging Actually Works | John Hally 28.01.2026 27λ

  If you work in medical imaging, DICOM is everywhere — even when you don’t see it.In this episode of Imaging Informatics Unplugged, Jason Nagels sits down with John Hally, Product Leader for Cloud and Technical Platforms at Merge, to break down what DICOM toolkits really do and why they matter more than ever as imaging expands beyond radiology.John brings over 15 years of medical imaging technical experience and more than a decade in product management, leading global strategy for the Merge VNA, DICOM and HL7 Toolkits, cloud platforms, and digital pathology. This conversation goes deep into the infrastructure that quietly powers interoperability across healthcare.What you’ll learn in this episode: • What a DICOM toolkit actually is (and what it isn’t) • Why AI vendors and new imaging startups often underestimate DICOM complexity • When organizations realize they need a toolkit instead of building DICOM from scratch • Open source vs commercial DICOM toolkits: tradeoffs, risk, and scalability • How cloud-native architectures, ARM processors, and containers are changing imaging platforms • Enterprise imaging beyond radiology: pathology, ophthalmology, scope video, and more • Why HTJ2K (High-Throughput JPEG 2000) is a big deal for large imaging datasets • How standards evolve and why “doing DICOM” doesn’t always mean doing it correctlyThis episode is essential listening for: • PACS administrators and imaging informatics professionals • AI companies entering healthcare imaging • Product managers and developers building imaging platforms • Clinical and technical leaders working on enterprise imaging strategies • Anyone preparing for CIIP or deepening their understanding of imaging standards👉 Like, subscribe, and turn on notifications so you don’t miss future episodes of Imaging Informatics Unplugged.🚀 Keep an eye on nagelsconsulting.com for upcoming courses focused on imaging standards, enterprise imaging, and real-world interoperability. Some big things are coming.
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  Interactive Multimedia Reporting: Standards, AI, & the Future of Reporting | Dr. Seth Berkowitz 15.01.2026 23λ

  In this episode of Imaging Informatics Unplugged, we explore Interactive Multimedia Reporting (IMR) and why traditional text-based radiology reports are no longer enough for image-driven clinical care.I’m joined by Seth Berkowitz, interventional radiologist, clinical informaticist, and Medical Director of Radiology Informatics at Beth Israel Deaconess Medical Center. Seth led the HIMSS–SIIM whitepaper work defining the technical challenges of vendor-neutral, interoperable multimedia reporting and is deeply involved in RSNA and IHE reporting standards.We discuss:• What Interactive Multimedia Reporting actually is (and what it isn’t)• Why most IMR implementations remain vendor-specific• The role of IHE profiles, FHIR, and DICOM in enabling interoperability• Real-world barriers to adoption, including workflow and reporting time• How AI and structured reporting could finally make IMR scalable• Why reports should behave more like modern digital tools, not static textThis conversation is for radiologists, imaging informatics professionals, CIIPs, vendors, and health IT leaders who care about clarity, interoperability, and the future of imaging communication.Referenced Papers:• Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: https://link.springer.com/article/10.1007/s10278-021-00450-5 • Interactive Multimedia Reporting Technical Considerations: https://link.springer.com/article/10.1007/s10278-022-00658-z 🔗 Learn more about our work and upcoming courses:https://www.nagelsconsulting.com🔗 Follow Nagels Consulting on LinkedIn:https://www.linkedin.com/company/nagels-consulting👍 If you found this episode useful, please like, subscribe, and share.💬 Let us know what imaging informatics topic you’d like us to tackle next.
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  The PACS Selection Blueprint: What Every Imaging Leader Gets Wrong | with Dr. Alex Towbin 08.12.2025 30λ

  Choosing a PACS can shape the next 10 years of your imaging department. Yet many leaders still rely on gut feel, vendor hype, or checklists that do not reflect real clinical operations. In this episode of Imaging Informatics Unplugged, I sit down with Dr. Alex Towbin, Professor of Radiology and the Neil D. Johnson Chair of Radiology Informatics at Cincinnati Children’s Hospital, to break down the complete PACS selection process behind his team’s widely referenced study. The PACS Selection Blueprint: What Every Imaging Leader Gets Wrong | with Dr. Alex TowbinWe cover every stage of the journey. The politics, the data, the shortcomings of vendor demos, and the real-world constraints that most hospitals underestimate. Dr. Towbin explains how his team evaluated 10 PACS vendors, how they neutralized bias within the committee, why radiologists often remain attached to their first PACS, and what PACS 3.0 will require as AI-driven workflows mature.If you are a PACS admin, enterprise imaging strategist, IT architect, radiology leader, or anyone preparing for an upcoming procurement, this episode will give you a clear and practical blueprint to follow.📝 Read the full published paperhttps://link.springer.com/article/10.1007/s10278-025-01672-7⸻In This Episode• How Cincinnati Children’s designed a transparent and data-focused PACS selection process• Why radiologists become “imprinted” on their first PACS and how that shapes department culture• The hidden cost of switching PACS and why minor improvements may not justify a complete replacement• How real anonymized data exposed significant weaknesses in vendor demos• What to expect in the shift toward PACS 3.0 with AI-enabled and automated workflows• Why database access, technologist workflows, and support models often matter more than shiny features• How to scale your procurement approach based on the impact of the system• What imaging leaders should rethink heading into their next PACS evaluation cycleChapters0:00 When your PACS feels tired – episode setup2:09 Meet Dr. Alex Towbin and the PACS selection study2:39 Why Cincinnati Children’s re-evaluated their PACS3:41 How radiologists get “imprinted” on their first PACS5:32 Alex’s journey across three generations of PACS6:14 Principles for a fair and transparent PACS selection8:53 Equal voice for radiologists, technologists, and IT11:24 Balancing data, emotion, and gut feel in decisions14:03 Sticking with the incumbent and the real cost of switching17:42 Asking vendors to load real anonymized pediatric data24:22 Lessons learned and how they would run the next RFP25:42 Support models, database access, and working with vendors30:50 Final advice⸻🎓 Advance your Imaging Informatics Skills• Courses: https://learn.nagelsconsulting.com• Consulting and Solutions: https://nagelsconsulting.com• Spotify: • LinkedIn: https://www.linkedin.com/company/nagels-consulting⸻🎧 Listen on Spotifyhttps://open.spotify.com/show/4DAxDXd0kpvRh6NwvSJiVF📺 Watch on YouTubehttps://www.youtube.com/@NagelsConsulting
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  Revolutionizing Patient Access to Medical Images | with Rishi Nayyar 29.10.2025 24λ

  In this conversation, Rishi Nayyar discusses his journey from banking to healthcare innovation, focusing on patient access to medical images and the role of AI in enhancing patient understanding and automating workflows. He emphasizes the importance of empowering patients with control over their data while also addressing the challenges of image exchange and the need for efficient non-clinical workflows. The discussion highlights the significance of building trust in AI solutions and the ongoing efforts to improve patient engagement and data management in healthcare.Takeaways:-Rishi Nayyar transitioned from banking to healthcare to address patient access to medical images.-The initial inspiration came from a personal experience with outdated technology in medical imaging.-Patient empowerment is crucial for reducing reliance on CDs for medical images.-Both patient-to-provider and provider-to-provider access are essential for effective image exchange.-Patients engage more with their health data during significant health issues.-AI can enhance patient understanding of medical reports through layman translations.-Automating non-clinical workflows can significantly improve efficiency in healthcare.-Data retention policies vary widely among healthcare organizations.-Trust in AI solutions is built through transparency and reliability.-Healthcare systems must adapt to new technologies while maintaining patient care quality.⸻🎓 Advance your Imaging Informatics Skills• Courses: https://learn.nagelsconsulting.com• Consulting & Solutions: https://nagelsconsulting.com• LinkedIn: https://www.linkedin.com/company/nagels-consulting⸻📺 Watch on YouTubehttps://www.youtube.com/@NagelsConsulting⸻
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  Introducing IIPCareers.com — The Imaging Informatics Career Tool 20.10.2025 19λ

  In this episode of Imaging Informatics Unplugged, Jasonwelcomes back Don Dennison, CIIP, FSIIM, and Mohannad Hussain for a deep dive into the evolution of the Imaging Informatics Career Development Matrix — nowreborn as a fully web-based platform at IIPCareers.com.The discussion continues from our last conversation(https://www.youtube.com/watch?v=7KIMlAsngBA&t=751s), where the team explored how imaging professionals can define, assess, and grow their skills inthis rapidly evolving field. In this episode:Don demos the new web interface that replaces the originalExcel matrix.Mohannad explains how the tool’s database-driven backendenables real-time updates and community feedback.Learn how to use role-based skill mapping, AI-poweredcareer planning, and prompt templates for job descriptions and quizzes.See how institutions can apply this tool to standardizejob roles, guide professional development, and identify training needs acrossimaging domains. Whether you’re a PACS admin, analyst, or imaging leader —this conversation will show you how to take control of your growth in imaginginformatics and empower your teams to do the same. ⸻ 🔗 Featured Resource 🧠 Explore the tool: https://iipcareers.com ⸻ 👏 Special Acknowledgment A huge thank you to Ghadeer Albattarni for her contributionsin helping develop the IIPCareers.com platform. Your expertise and collaboration helped bring this resource to life!⸻🎓 Advance your Imaging Informatics SkillsCourses: https://learn.nagelsconsulting.comConsulting & Solutions: https://nagelsconsulting.comLinkedIn:https://www.linkedin.com/company/nagels-consulting⸻📺 Watch on YouTubehttps://www.youtube.com/@NagelsConsulting⸻ 📌 Stay Connected🌐 Website: https://www.nagelsconsulting.com🎓 Courses: https://learn.nagelsconsulting.com🔗 LinkedIn:https://www.linkedin.com/company/nagels-consulting ⸻
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  AI in Radiology: Hype, Reality, and What Comes Next | with Dr. Woojin Kim 11.09.2025 50λ

  What happens when large language models (LLMs) move from casual conversations into clinical practice? In this episode of Imaging Informatics Unplugged, Dr. Woojin Kim joins Jason Nagels to explore the hype, reality, and future of AI in radiology.Dr. Kim is the Chief Strategy Officer and CMIO at HOPPR, Chief Medical Officer at the ACR Data Science Institute, and a musculoskeletal radiologist at the Palo Alto VA Medical Center. He’s also a proven entrepreneur, co-founding Montage Healthcare Solutions and Equium Intelligence, both acquired by major players in imaging AI.We cover: How Dr. Kim consumes 4–6 hours of AI research daily using tools like Notebook LM and Recall  The underestimated risk of silent degradation in clinical AI models and why continuous monitoring matters  The difference between prompt engineering vs. context engineering for PACS and RIS integration  Opportunities and pitfalls of structured reporting with LLMs  Critical discussions on bias, fairness, and synthetic data in radiology AI  Where Dr. Kim sees the field heading: multimodal models, agentic AI, and the evolving role of the radiologist If you’ve ever wondered how much we can really trust AI in clinical imaging, this episode offers an unfiltered look at the challenges and opportunities shaping the next decade.🔔 Don’t forget to like, subscribe, and hit the bell to stay updated on future episodes exploring the intersection of imaging, informatics, and AI.👉 Learn more at:🌐 https://www.nagelsconsulting.com📘 https://learn.nagelsconsulting.com
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  From Interfaces to Impact: Why Healthcare Still Struggles with Interoperability 15.08.2025 36λ

  🎙️ Featuring Toni Skokovic | Imaging Informatics Unplugged | Ep. 2Why is healthcare interoperability still so difficult—even with mature standards like HL7, DICOM, and FHIR? In this episode of Imaging Informatics Unplugged, Jason sits down with Toni Skokovic, a 25-year health tech veteran and founder of YourRevenuePlan, to explore what’s really blocking progress.Whether you’re a provider, vendor, PACS admin, or health IT strategist, this conversation goes deep on what’s broken—and what we can finally do about it.🔍 What we cover in this episode: • Why “integration” ≠ interoperability • The real cost of automation without alignment • How agentic AI fits into healthcare workflows (and where it doesn’t) • Why adoption still fails after go-live • Practical steps for providers to tackle real workflow bottlenecks • How vendors can sell solutions instead of specs • The importance of measuring outcomes, not just implementing features📅 UPCOMING TRAINING:Join us for EIB_110: Enterprise Imaging for Business Professionals, a 1-day course split across two half-day live sessions on September 17th and 18th.Ideal for imaging IT teams, clinical ops leaders, and vendor staff.📘 Register here → https://learn.nagelsconsulting.com/course/eib-110-september—🔔 Don’t forget to like, subscribe, and hit the bell to stay updated on future episodes exploring the intersection of imaging, informatics, and AI.👉 Learn more at:🌐 https://www.nagelsconsulting.com📘 https://learn.nagelsconsulting.com🔗 https://www.linkedin.com/company/nagels-consulting#HealthcareInteroperability #ImagingInformatics #HealthIT #EnterpriseImaging #FHIR #DICOM #HL7 #AgenticAI #PACS #MedicalImaging #DigitalHealth