Radiology Rewired

What’s Next Tech? | Radiology Rewired

In Episode 5 of Radiology Rewired, Dr. Vivek Singh speaks with Dr. David Stoffel, Chief Business Officer at RapidAI, about what changes when clinical AI moves beyond pilots and becomes part of a hospital’s core infrastructure.Rather than focusing on whether AI works, the conversation looks at how hospitals operationalize AI at scale. Dr. Stoffel shares perspective from across medicine, business, and healthcare technology to explain why leadership decisions, governance models, and shared access determine whether AI actually shapes clinical decisions.Topics include how hospitals move from isolated tools to platforms, why efficiency gains alone rarely justify investment, and what it takes to embed AI into real workflows without adding friction for clinicians.This episode offers a practical, execution-focused view of how clinical AI becomes a durable, system-wide capability inside complex healthcare organizations.Chapters:00:00 – Introduction: when clinical AI becomes infrastructure01:10 – Moving beyond AI pilots02:30 – Why efficiency does not equal impact03:50 – How hospitals operationalize AI decisions05:10 – Leadership, governance, and shared access06:40 – Why integration across specialties matters08:15 – Stroke care as an infrastructure model10:05 – Predictive AI and decision-making at scale12:20 – Avoiding cognitive burden in AI deployment14:20 – What clinical AI looks like when it sticks

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The AI Ripple | Radiology Rewired

Feb 901:04:26Tap to summarize

In this episode of Radiology Rewired, Dr. Vivek Singh sits down with Dr. Samir Kumar, chief medical officer and former nephrologist, to explore why clinical AI succeeds in some health systems and fails to deliver value in others.The conversation takes a system-level view of AI adoption, examining how leadership alignment, cross-specialty integration, and measurable return on investment determine whether AI meaningfully improves care or quietly stalls. Dr. Kumar shares the administrative perspective on evaluating clinical AI tools, including why many solutions demonstrate efficiency gains but never translate into sustained impact.They also discuss how poorly integrated AI can increase cognitive burden for clinicians, while thoughtfully implemented systems can reduce mental strain, support earlier decision-making, and improve coordination across care teams without undermining clinical judgment.This episode offers practical insight for physicians, health system leaders, and anyone involved in healthcare AI adoption.Chapters:00:00 — Introduction: why clinical AI adoption often stalls01:15 — Efficiency vs. value in healthcare AI02:45 — How health systems evaluate AI ROI04:20 — The role of leadership and governance in AI adoption06:10 — Why cross-specialty integration matters08:05 — Stroke care as a model for AI-enabled coordination10:10 — Clinical decision support and predictive AI models12:30 — Reducing cognitive burden for clinicians14:20 — What successful clinical AI looks like at scaleWatch on YouTube!

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The Future Workforce | Radiology Rewired

Jan 2001:17:04Tap to summarize

Radiology remains one of the most essential specialties in modern medicine, yet many medical students and residents still hear the same warning: “AI is coming for radiologists.” That perception is shaping career decisions, training experiences, and how the next generation approaches image interpretation.In episode 3 of Radiology Rewired, Dr. Vivek Singh is joined by neuroradiologists Dr. Dhairya Lakhani and Dr. Vivek Yedavalli to discuss how AI is already influencing radiology training and why the real risk is not automation, but how future radiologists are taught to work with AI.The conversation focuses on trainees and early-career radiologists. The group explores how poorly designed AI can increase cognitive burden, encourage cognitive offloading, and weaken independent clinical reasoning if it is introduced without guardrails. They also discuss how AI can support learning when it provides context early, reinforces pattern recognition, and helps trainees focus on what matters most.This episode offers a clinician-led perspective on how residency programs, departments, and health systems can adopt AI in ways that strengthen training, protect clinical judgment, and build a more sustainable radiology workforce.Chapters00:00 – Introduction: why students still worry about AI01:30 – Imaging demand, burnout, and training pressure03:45 – How AI narratives affect the radiology pipeline06:10 – Cognitive burden and cognitive offloading in trainees09:00 – Using AI to support learning without replacing judgment11:45 – Workflow design and training-first systems14:30 – What residents need to graduate confident and independent17:30 – Preparing the next generation of radiologistsListen on Apple Podcasts and SpotifyRead the full episode recap on the RapidAI blog: (enter blog URL here)

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Workflow or Overload? | Radiology Rewired

Dec 2201:35:20Tap to summarize

In the second episode of Radiology Rewired, Dr. Singh sits down with Dr. Jeremy Heit, Director of Neuroimaging and Neurointerventional Services at Stanford, to explore the most pressing challenges facing radiology today, from workforce shortages to the rising burden of imaging demand.Dr. Heit discusses why 39 percent of radiologists are considering leaving the field, how private equity consolidation is reshaping career paths, and why medical students remain wary of radiology amid rapid advances in AI. He also breaks down how imaging volumes have accelerated far faster than the radiology workforce, and why deep clinical AI will be essential to closing this widening gap.This episode offers an unfiltered look at burnout, practice pressures, and the evolving role of AI in radiology.Chapters:00:00 — Introduction: workforce pressures and burnout01:00 — The collapse of traditional private practice models02:15 — Why medical students fear entering radiology03:56 — Understanding the 39 percent burnout statistic05:23 — How AI has reshaped stroke care and transfer decisions08:30 — Imaging volume growth vs. workforce capacity11:00 — What makes an AI tool clinically valuable13:00 — The future of AI adoption and physician-led innovationListen on Apple Podcasts and SpotifySee this episode on Youtube!

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AI Meets Imaging | Radiology Rewired

Nov 3001:12:12Tap to summarize

In the debut episode of Radiology Rewired, RapidAI’s new clinician-led podcast hosted by Dr. Singh, Dr. Greg Albers—founder of RapidAI and professor of neurology at Stanford University—shares the story behind one of medicine’s most important breakthroughs: extending the stroke treatment window to 24 hours.Dr. Albers recounts how decades of imaging research led from early diffusion-perfusion MRI to automated AI tools that changed global stroke guidelines and transformed how teams diagnose and treat patients.This episode explores how deep clinical AI is reshaping radiology and why credible, clinician-led innovation remains essential for the next generation of healthcare technology.Chapters:00:00 – Introduction: Will AI replace radiologists?02:15 – The origins of stroke imaging research06:40 – How diffusion and perfusion imaging redefined treatment12:10 – The creation of RapidAI and clinical automation18:00 – Why AI reinforces, not replaces, radiologists25:00 – The future of explainable AI in stroke careListen on Apple Podcasts and SpotifySee this episode on Youtube!Learn more: rapidai.com/podcast/radiology-rewired

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