What’s Next Tech? | 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 infrastructure 01:10 – Moving beyond AI pilots 02:30 – Why efficiency does not equal impact 03:50 – How hospitals operationalize AI decisions 05:10 – Leadership, governance, and shared access 06:40 – Why integration across specialties matters 08:15 – Stroke care as an infrastructure model 10:05 – Predictive AI and decision-making at scale 12:20 – Avoiding cognitive burden in AI deployment 14:20 – What clinical AI looks like when it sticks

The AI Ripple | Radiology Rewired

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 stalls 01:15 — Efficiency vs. value in healthcare AI 02:45 — How health systems evaluate AI ROI 04:20 — The role of leadership and governance in AI adoption 06:10 — Why cross-specialty integration matters 08:05 — Stroke care as a model for AI-enabled coordination 10:10 — Clinical decision support and predictive AI models 12:30 — Reducing cognitive burden for clinicians 14:20 — What successful clinical AI looks like at scale Watch on YouTube [https://youtu.be/rZZvVGHzQ14]!

The Future Workforce | Radiology Rewired

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. Chapters 00:00 – Introduction: why students still worry about AI 01:30 – Imaging demand, burnout, and training pressure 03:45 – How AI narratives affect the radiology pipeline 06:10 – Cognitive burden and cognitive offloading in trainees 09:00 – Using AI to support learning without replacing judgment 11:45 – Workflow design and training-first systems 14:30 – What residents need to graduate confident and independent 17:30 – Preparing the next generation of radiologists Listen on Apple Podcasts and Spotify Read the full episode recap on the RapidAI blog: (enter blog URL here)

Workflow or Overload? | Radiology Rewired

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 burnout 01:00 — The collapse of traditional private practice models 02:15 — Why medical students fear entering radiology 03:56 — Understanding the 39 percent burnout statistic 05:23 — How AI has reshaped stroke care and transfer decisions 08:30 — Imaging volume growth vs. workforce capacity 11:00 — What makes an AI tool clinically valuable 13:00 — The future of AI adoption and physician-led innovation Listen on Apple Podcasts and Spotify See this episode on Youtube [https://youtu.be/j0g2W5Z3uN4]!

AI Meets Imaging | Radiology Rewired

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 research 06:40 – How diffusion and perfusion imaging redefined treatment 12:10 – The creation of RapidAI and clinical automation 18:00 – Why AI reinforces, not replaces, radiologists 25:00 – The future of explainable AI in stroke care Listen on Apple Podcasts and Spotify See this episode on Youtube [https://www.youtube.com/watch?v=fsmQsPGWmQI]! Learn more: rapidai.com/podcast/radiology-rewired