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!

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)

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!