Scanning the Market

Projectus Consulting
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Scanning The Market dives straight into the business behind Radiology innovation. Hosted by Jay Gurney, every episode delivers real talk with the founders, investors, and visionaries shaping the Medical Imaging and AI landscape. This is where ambition meets economics. It’s fast, opinionated, and built for people who want to understand where the money, talent, and opportunity are really moving. Forget the corporate filter. These are honest conversations about risk, growth, and the grind of building something in Radiology that actually succeeds.

  1. 3d ago

    Enterprise Radiology AI: Scaling Beyond Stroke with David Stoffel | Scanning the Market

    In this episode of Scanning the Market, host Jay Gurney sits down with David Stoffel, Chief Business Officer at RapidAI. Drawing from his extensive commercial experience across surgical robotics, cardiac monitoring, and point-of-care medical devices, David shares what it takes to transition from a single-finding point solution into a true enterprise healthcare platform. David discusses how RapidAI expanded beyond its origin as a stroke imaging tool to tackle enterprise workflow orchestration, care completion, and multi-disease clinical decision support. He breaks down the realities of deploying AI across 2,500+ hospitals, managing hybrid edge/cloud infrastructure, reducing radiologist cognitive load, and why white-glove clinical support is essential for scaling healthcare technology. Key Discussion Points: Great Products vs. Clinical Change: Lessons learned from scaling surgical robotics and medical devices into real-world hospital workflows.Defining the Enterprise Platform: Why moving beyond isolated point solutions requires solving care coordination, workflow orchestration, and enterprise-wide integration.Care Completion: Why early detection isn't enough and how software must assist clinicians through treatment and follow-up management.Hybrid Infrastructure (Edge vs. Cloud): Balancing low-latency, resilient local processing for time-critical emergencies (like stroke) with scalable cloud compute.Overcoming Pilot Trap: What separates health systems that successfully scale AI enterprise-wide from those stuck in perpetual local testing.The Human Element of AI Deployment: Why white-glove clinical support and customer success teams matter as much as the underlying algorithms.Timestamps: 00:00 – Introduction & Welcoming David Stoffel 01:31 – Lessons from Intuitive Surgical & Scaling HealthTech 04:12 – Diagnostic vs. Workflow AI: Delivering Real Value 08:46 – Moving from Point Solutions to Enterprise Care Completion 13:08 – Solving the 300-Algorithm Fragmentation Problem 16:26 – Is Infrastructure Layer Becoming More Important Than the Algorithm? 18:52 – Designing the Ideal Radiologist Workflow & Reducing Cognitive Burden 25:16 – Hybrid Edge vs. Cloud Processing for Time-Sensitive Care 30:28 – What Happens When You Scale AI to 2,500+ Hospitals? 32:53 – What Separates Enterprise Scaling from Perpetual Hospital Pilots? 35:36 – White-Glove Support & The People-First Approach at RapidAI 40:42 – Repositioning RapidAI: Looking Forward to the Next 5 Years #Radiology #HealthcareAI #HealthTech #RapidAI #EnterpriseAI #MedicalImaging #ClinicalWorkflow #ScanningTheMarket

  2. Oct 14

    What the 1990s PACS Revolution Teaches Us About AI | Dr. Eliot Siegel

    In this special SIIM-CAIMI series episode of Scanning the Market, host Jay Gurney sits down with Dr. Eliot Siegel—Professor of Diagnostic Radiology at the University of Maryland, founding physician at United Theranostics, and the pioneer who led the Baltimore VA to become the world’s first filmless digital hospital in 1993.Drawing on over three decades of imaging informatics leadership, Dr. Siegel compares the early pushback against PACS to today’s debate around artificial intelligence. He discusses why claims that AI will replace radiologists mirror the 1990s fears that digital workstations would eliminate the specialty, why model drift and isolated benchmarks demand "economic realism," and why the real future of clinical AI lies in cross-correlating imaging with full patient context from the electronic health record.Key Topics Covered:-The Filmless Revolution (1993): Overcoming inertia, legal pushback, and the myth that digital viewing would end radiology.-Deja Vu in Healthcare AI: Why predictions that AI will replace physicians fail to account for clinical complexity and real-world workflows.-Lessons from IBM Watson: Why passing standardized exams and answering isolated questions does not translate to clinical practice.-Economic Realism & Model Drift: The brittle nature of static neural networks and the hidden costs of evaluating algorithms across diverse patient populations.-Moving Beyond Pixels to Full Context: How integrating lab values, genomics, prior notes, and serial scans into reporting transforms precision oncology and theranostics.-The Future of Medical Learning: Why the FDA and industry must shift toward continuously learning, adaptive AI models rather than static software deployments.-CAIMI 2026 Preview: Bridging the gap between student developers, clinical researchers, and enterprise deployment in Philadelphia.

  3. Oct 7

    Will Radiologists Build Their Own AI? | Dr. Peter Chang

    In this special SIIM-CAIMI series episode of Scanning the Market, host Jay Gurney sits down with Dr. Peter Chang—radiologist, software engineer, co-founder of the Center for Artificial Intelligence in Diagnostic Medicine (CAIDM) at UC Irvine, and CAIMI co-chair.Dr. Chang breaks down a startling shift in medical software development: why 99% of his code is now AI-generated and what happens to healthcare innovation when the barrier to building complex software collapses. Moving past the mechanics of coding syntax, this discussion examines why narrow, expensive point solutions will face margin compression, how the value chain is pivoting toward orchestration and native workflows, and why clinical governance must evolve to evaluate non-deterministic, agentic systems.Key Topics Covered in This Episode:-99% AI-Generated Code: How coding agents shifted from toy tools to production-grade accelerators capable of building medical vision models over a weekend.-The Crucial 1%: Why human engineering principles, architectural steering, and domain intuition matter more than syntax and prompt engineering.-Democratizing Medical Software: What happens when clinicians can pull open-source models, tweak repositories, and solve niche clinical problems directly.-The Death of Overpriced Point Solutions: Why charging hundreds of thousands of dollars for narrow, single-finding models is becoming economically unsustainable.-Shifting Value to Orchestration: Why the algorithm itself is becoming a commodity while routing, data flow, inference scaling, and PACS/reporting integration capture value.-Dynamic vs. Deterministic Governance: The friction between static FDA clearances and real-world clinical software that auto-heals and updates continuously.-Evaluating the Human-AI Team: Moving beyond isolated model benchmarks to evaluate how specific clinicians perform when paired with AI assistants.-CAIMI 2026 Preview: Exploring the AI Builder Showcase and creative agentic workflows in Philadelphia.

  4. Sep 30

    How Health Systems Actually Deploy and Govern AI | Dr. Katherine Andriole

    In this special SIIM-CAIMI series episode of Scanning the Market, host Jay Gurney sits down with Dr. Katherine (Kathy) Andriole—Associate Dean for Health AI Strategy and Innovation at the David Geffen School of Medicine at UCLA and former Director of Academic Research and Education at Mass General Brigham AI.With decades of experience driving clinical AI implementation and imaging informatics, Dr. Andriole moves beyond the excitement of standalone algorithms to examine how health systems must build coherent enterprise AI strategies. She breaks down why healthcare must look beyond narrow detection tools to workflow and risk prediction, how cross-functional governance models evaluate risk vs. innovation, and what it takes to deploy, validate, and monitor clinical tools at enterprise scale.Key Topics Covered in This Episode:-Stepping Beyond Radiology: What the broader enterprise can learn from radiology’s early adoption mistakes, including avoiding narrow one-off deployments and insisting on standards like DICOM and HL7 FHIR.-Enterprise Strategy vs. AI Projects: Why health systems need cohesive strategy over isolated tools, moving from pure detection to risk prediction, agentic AI, and back-office efficiency.-Two-Tier Governance in Practice: Balancing patient privacy, security, and innovation using tiered evaluation frameworks (from routine collaborations to enterprise data sharing).-Redefining Clinical ROI: Why AUC and model accuracy are not ROI, and how real value must be measured in clinician work-life, length of stay, and patient safety.-Post-Deployment Realities: Collective responsibility in real-time model monitoring, testing via shadow mode pipelines, and preventing model drift.-AI Literacy and "No-Skilling": Designing education frameworks for medical students and residents that teach foundational AI mechanics without eroding core clinical judgment.-Translational Research & CAIMI 2026: Bridging the final gap between lab models and real-world clinical workflows through true vendor-hospital partnerships.

  5. Sep 28

    The App Store for Radiology AI: Scaling Adoption with Dhruv Sahai | Scanning the Market

    In this episode of Scanning the Market, host Jay Gurney sits down with Dhruv Sahai, Chief Operating Officer and "AI Matchmaker" at CARPL.ai. Transitioning from corporate M&A law and supply chain risk consulting into healthtech, Dhruv shares an operator’s perspective on what it really takes to scale clinical AI adoption across global healthcare systems.Dhruv breaks down why having hundreds of AI point solutions isn't the problem—the real bottleneck is the lack of seamless validation, single-layer integration, and post-deployment monitoring. From CARPL's $10M Series A round led by the World Bank's IFC to enabling multi-specialty expansion beyond radiology, this conversation cuts through the sales hype to focus on clinical sustainability and patient access.Key Topics Covered in This Episode:From Corporate Law to HealthTech COO: How a background in cross-border M&A and regulatory compliance translates to running an enterprise AI platform.The "App Store" Model for Radiology: Why CARPL operates as an open marketplace of 300+ solutions rather than a walled garden.The Full AI Lifecycle: Moving beyond deployment into retrospective validation on local hospital data and real-time post-deployment monitoring.Partners vs. Vendors: Why CARPL shares its sales pipeline with algorithm developers and acts as an enablement bridge rather than a transactional reseller.The Clinical vs. Commercial Divide: What separates clinically accurate models from sustainable, commercial products that fit radiologist workflows.Funding & Global Scaling: What a $10M Series A backed by the IFC means for expanding across emerging and mature healthcare markets.The Impact of Foundation Models: Why generalist models complement enterprise deployment platforms instead of replacing them.

  6. Sep 24

    Beyond Model Performance: What Actually Drives Imaging AI ROI? | Dr. Hari Trivedi

    In this special SIIM-CAIMI series episode of Scanning the Market, host Jay Gurney sits down with Dr. Hari Trivedi—Associate Professor of Radiology and Biomedical Informatics and AI Council Chair at Emory Healthcare. Together, they cut through the hype around model performance metrics to examine whether imaging AI is finally delivering measurable clinical and financial ROI. Dr. Trivedi breaks down why high-performing diagnostic algorithms often fail to prove positive financial returns in US fee-for-service systems, why non-interpretive and acquisition models see faster adoption, and what it really costs health systems to run prospective validation and continuous post-deployment monitoring. Key Discussion Points:Redefining ROI in Healthcare: Why diagnostic detection models struggle to show direct dollar returns compared to non-interpretive tools (impression generation, report structuring, and scan acceleration). The Fee-for-Service Dilemma: The fundamental misalignment between patients, health systems, and payers when AI reduces unnecessary tests or procedures. Emory's Evaluation Process: How Emory’s AI Council screens commercial solutions, why 510(k) summaries leave critical gaps, and how peer feedback often outweighs marketing data. The Reality of Post-Deployment Monitoring: The hidden operational costs, data harmonization challenges, and lack of commercial incentives for long-term model surveillance. The Death of Narrow Point Solutions: Why health systems are moving away from single-finding models toward unified enterprise platforms and native PACS integrations. The Shift from Science to Practical Value: Why vendor booths at SIIM, RSNA, and CAIMI have moved away from AUC numbers toward operational impact and workflow efficiency.

  7. Sep 16

    Can AI solve radiology's capacity challenge? With Dr. Tessa Cook

    In this episode of Scanning the Market: CAIMI Conversations, I sit down with Dr. Tessa Cook, Associate Professor of Radiology at Penn Medicine, to ask a pretty simple question with a much harder answer:   Can AI actually solve radiology’s capacity challenge?   We get into where the real bottlenecks are today, why the biggest productivity gains may come from everything around image interpretation rather than the image itself, and what tools like generative EHR search, ambient reporting and foundation models could realistically change inside the reading room.   We also cover the less glamorous but more important part of AI adoption: workflow friction, trust, monitoring, model drift, governance and whether efficiency gains actually improve life for radiologists or simply result in more work.   A few of the areas we cover: Why rising imaging volumes and workforce pressure have created a capacity problem that recruitment alone probably won’t solveWhere AI is already saving time in clinical workflowsWhy non-interpretive tasks may deliver more immediate value than another detection algorithmHow generative AI and foundation models could reshape reporting and workflowWhat good AI governance and post-deployment monitoring should actually look likeThe balance between productivity, quality and radiologist wellbeing  This episode is also part of our wider partnership with SIIM ahead of CAIMI 2026, taking place October 26–27 in Philadelphia.   CAIMI is built around exactly these kinds of conversations: what is genuinely working in imaging AI, what still needs to be pressure-tested, and how we move from interesting research into something that actually survives inside a health system.   We’ll be releasing more conversations with leaders across imaging AI in the run-up to the event.   #ScanningTheMarket #CAIMI2026 #SIIM #RadiologyAI #ImagingAI #HealthcareAI

  8. Sep 3

    How Hospitals Buy, Deploy & Measure AI with Peter Eason | Scanning the Market

    In this episode of Scanning the Market, host Jay Gurney sits down with Peter Eason from Ferrum Health to discuss the realities of buying, deploying, and governing clinical AI in modern health systems. Moving beyond marketing buzzwords, Peter shares an operations and finance perspective on how hospitals actually evaluate software, quantify return on investment, and make AI stick in clinical workflows.Key Discussion Points: - Finance to Healthcare Ops: What transition from investment banking to healthcare ops reveals about venture capital, unit economics, and sustainable business models. - The "Last-Mile" Problem: Why deploying individual AI point solutions creates duplicative infrastructure costs—and the case for an enterprise enablement fabric. - Observability & Ground Truth: Moving past static FDA clearances to measure model performance, drift, and clinical efficacy on local hospital data in real time. - What Hospital CFOs Actually Care About: Navigating reimbursement codes, cost avoidance, incidental findings, and realistic ROI models. - Practical AI Governance: Why healthcare doesn't need another theoretical framework, but rather automated tools to monitor what is actually happening post-deployment. - Market Outlook: The rise of foundation models in clinical settings and why spending on data analytics and observability will outpace new point solutions. #Radiology #HealthcareAI #HealthTech #MedicalImaging #ClinicalAI #FerrumHealth #ScanningTheMarket

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About

Scanning The Market dives straight into the business behind Radiology innovation. Hosted by Jay Gurney, every episode delivers real talk with the founders, investors, and visionaries shaping the Medical Imaging and AI landscape. This is where ambition meets economics. It’s fast, opinionated, and built for people who want to understand where the money, talent, and opportunity are really moving. Forget the corporate filter. These are honest conversations about risk, growth, and the grind of building something in Radiology that actually succeeds.

Information

  • Creator
    Projectus Consulting
  • Years Active
    2025 - 2026
  • Episodes
    18
  • Rating
    Clean
  • Copyright
    © Projectus Consulting
  • Show Website
    Scanning the Market