AI Rounds by the Cumming School of Medicine

If a learner rehearses a difficult conversation with an AI, what exactly are they practising against? A listener asked, and the answer runs through two layers of embedded value: the persona an educator or student writes, and the model generating everything underneath it. This episode covers what the survey evidence shows about that second layer, why the stakes rise sharply as an encounter becomes more values-laden, and the case for...

What problem did we say we were solving, and what would we have done about it if AI had never existed? That is the question we'll tackle this episode. Not whether AI tools work — they do, but that is what makes the cases interesting. Solutionism is the error of letting the available solution decide which problem we have, and once you know that it is easy to see AI solutionism all around us. So, why does it keep happening? Wel...

Note: This episode was recorded on July 28, 2026 and discusses ongoing litigation which may have changed by release. On July 22, 2026, a Florida pastor filed suit against OpenAI, alleging ChatGPT talked him out of seeking care for what turned out to be a pulmonary embolism. On July 23, OpenAI made its health product generally available to every adult in the United States. Those two dates are a fair picture of where we are.

46% of Ca...

Many of you have written in asking about OpenEvidence. In this episode I walk through what's actually inside the AI clinical search tool that didn't exist before 2023 and is now used by an estimated two-thirds of US physicians. I cover how it differs from ChatGPT, UpToDate, and Google Scholar, how its retrieval-augmented architecture works, where its content comes from, and how the two search modes, standard Consult and Deep Consul...

Most conversations about AI in medicine assume a patient in the room. This episode doesn't. Jessalyn talks with Joshua Samsoondar, a pathology resident at the University of Calgary, about what AI looks like from inside a non-patient-facing specialty — screening lymph nodes for cancer, using AI to catch up on unfamiliar terminology, and the tradeoffs built into decisions most patients never see happen. Josh makes the case that...

Most conversations about AI in medical education are led by faculty. This one isn't. Hailee Rochon, a registered nurse and a student in the University of Calgary's Master of Physician Assistant Studies program, got in touch because she wanted to share how AI has changed the way she learns.

For Hailee, the value isn't speed or shortcuts. It's self-direction. She describes using AI to follow her own curiosity further than a syllabus a...

When a colleague was using an AI tool and it unexpectedly swore at him, his reaction caught him off guard. It wasn’t amusement or confusion — it was genuine discomfort. And that raised a question worth exploring: why do we have such strong emotional responses to AI behaviour?

In this episode, Dr. Kannin Osei-Tutu and I dig into the research on how humans relate to AI systems. We cover the CASA paradigm — the findin...

For decades, we've understood human reasoning through two systems: the fast, intuitive one and the slow, deliberate one. But that framework was built before AI became a thinking partner. In this episode, I sit down with Dr. Steven Shaw — a Canadian scholar and postdoctoral fellow at the Wharton School — to talk about his new framework, Tri-System Theory, and what it means that AI now functions as a third cognitive syste...

Agents went from a concept most faculty hadn't encountered to embedded infrastructure in the tools you already use — in roughly 18 months. In this episode, I trace what changed, why it changed so fast, and what a three-tier framework for thinking about AI autonomy can tell you about what you're already working with.

Microsoft & Health Management Academy: Agentic AI readiness in healthcare — 43% piloting, 3% deployed ...

Most conversations about AI in medical education focus on efficiency — faster feedback, streamlined assessment, reduced administrative burden. Dr. Nia Abdullayeva asks a harder question: what happens to the human dimensions of training in the process? In this episode, Nia joins me to explore how AI, used intentionally, can protect rather than displace the relational time that makes good teaching and good medicine possible. We...

Not all AI tools work the same way — and NotebookLM is a clear example of why that distinction matters. In this episode, I walk through how NotebookLM works, why its underlying architecture makes it meaningfully different from general-purpose AI assistants, and what that means for how you should use it. We cover the full range of what the tool can produce — from cited Q&A and study guides to AI-generated audio overv...

AI agents are everywhere now — tools that can manage your email, execute research tasks, book appointments. But before we explore what AI agents can do for you, we need to understand what you're actually trading every time you use AI.

In this Season 2 opener, I walk through the major trade-offs faculty face when using AI in research, education, and clinical work: efficiency versus depth, breadth versus expertise, automation ve...

In an era where AI tools promise to accelerate every aspect of academic work, graduate students face a paradox: having access to powerful technology while needing to develop fundamental research skills.

In this episode, Inara Lalani, a current graduate student shares insights about the critical importance of discernment in AI use. The conversation explores prioritizing process over product in the learning environment, developing fra...

What if your AI could work independently toward your goals instead of just answering individual questions? In this episode, we explore AI agents—autonomous systems that can monitor information, make decisions, and take actions without constant oversight.

Unlike traditional AI tools that respond to single requests, agents operate continuously to achieve specific objectives. For medical faculty, this means AI that can monitor re...

Feeling overwhelmed by AI in education? You've been here before.

In this conversation, Dr. D'Arcy Norman draws on three decades of educational technology experience to reveal a striking pattern: roughly every ten years, a "revolutionary" technology emerges that promises to transform education forever. Computers. The Internet. MOOCs. And now, AI.

Each time, the same fears surface. Each time, vendors promise disruption and personalizat...

Why do GenAI systems confidently state incorrect medical facts instead of saying "I don't know?" Groundbreaking research from OpenAI and Georgia Tech reveals that AI hallucinations aren't bugs to be fixed—they're inevitable consequences of how these systems are trained. This episode explores the "singleton problem" that makes AI systematically unreliable on rare facts, connects to our previous discussion of AI benchmark satur...

What happens when artificial intelligence collides with centuries-old academic traditions? In this thought-provoking episode, Dr. Heather Jamniczky, Associate Dean of Graduate Science Education and 3M National Teaching Fellow, tackles the seismic shifts reshaping how we train the next generation of medical researchers.

From late-night worries about academic integrity to bold visions of AI-ready graduates, Heather shares candid insig...