Podcast
AI Rounds by the Cumming School of Medicine
Hosted by Office of Faculty Development, Cumming School of Medicine, University of Calgary · EN
AI Rounds is an educational podcast designed for university faculty in medicine and health sciences navigating the evolving landscape of artificial intelligence in healthcare and education. Each episode breaks down complex AI concepts into digestible insights, exploring practical applications and discussing how these technologies are reshaping medical research and education. Join us as we examine AI through a beginner's lens, creating a space where faculty can grow their understanding of these powerful tools that are transforming healthcare delivery, research, and education.
17episodes
Episodes
Newest firstAll episodes
OpenEvidence: An Overview
2w ago00:25:37Tap to summarizeMany 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 Consult, perform in the emerging evaluation literature. I take on the trust question directly and land on a five-question framework for evaluating any AI evidence tool. And I close with what we as a profession aren't talking about enough: the enterprise Epic integrations at Mount Sinai, Sutter Health, and Cedars-Sinai, patient awareness, structural dependence on a single tool, and the business model.The examples are clinical because OpenEvidence is a clinical tool. But the framework and the harder questions apply as much to the AI tools you use in research and teaching, and I've tried to make that portability explicit.
Transcribe →Beyond the Bedside: AI in Pathology
4w ago00:38:07Tap to summarizeMost 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 AI can sharpen efficiency and training without replacing the judgment pathologists bring to the work. For clinicians, researchers, and educators alike, it's a look at a part of medicine that AI is already reshaping, quietly.Reach out to Dr. Samsoondar at joshua.samsoondar@ucalgary.ca
Transcribe →Learning on Your Own Terms: A Student Led Conversation About AI
Jul 100:24:06Tap to summarizeMost 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 allows, and to work on the specific areas where she knows she's struggling, at her own pace. It's a picture of a motivated learner setting the agenda, with AI as the tool that makes that practical.It's also a considered picture. Hailee is clear about where she doesn't rely on these tools, and how she checks what they tell her. This episode offers a chance for faculty to hear how at least one student is really learning with AI.
Transcribe →The Character We Create: Anthropomorphizing AI
Jun 1700:30:35Tap to summarizeWhen 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 finding that we automatically apply social rules to computers the same way we do to people — and what happens when the “character” we’ve built for an AI tool suddenly breaks. We discuss the uncanny valley effect in text-based AI, the paradox that making AI feel more human-like can backfire, and the flip side of the coin: automation bias, where we trust AI too much.Kannin reflects on what his experience revealed about his own assumptions, and we close with a challenge: pay attention to your emotional reactions when using AI tools this week. What patterns emerge? What character have you built?
Transcribe →Fast, Slow, and Artificial — a Conversation With Dr. Steven Shaw on AI and Cognitive Surrender
Jun 300:35:29Tap to summarizeFor 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 system operating outside the brain.Shaw coined the term "cognitive surrender" to describe what happens when we adopt AI outputs without critical evaluation — not as a deliberate choice, but as a quiet default. We get into how it differs from simply using AI as a tool and what it looks like across clinical documentation and graduate training. Plus a practical challenge to close.Dr. Shaw's preprintDr. Shaw's websiteKnowledge at Wharton podcast
Transcribe →Agents: How to Give AI a Job
May 2000:25:21Tap to summarizeDelegating to an AI agent is not the same as prompting a chatbot. In this episode, I walk through what good delegation actually looks like — scope, constraints, and checkpoints — and apply it to three concrete tasks: updating your CV, building a simple course assistant, and screening abstracts for a scoping review. The framework is practical, the failure modes are real, and the limits matter.
Transcribe →The Year Agents Got Real
May 600:19:10Tap to summarizeAgents 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 in live workflows. NEJM, January 2026. Cao W, Zhang Q, Liu J, Liu S. From Agents to Governance: Essential AI Skills for Clinicians in the Large Language Model Era. J Med Internet Res. 2026;28:e86550.
Transcribe →Could AI Bring Back Humanism in Medical Education?
Apr 2200:30:05Tap to summarizeMost 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 get into the cognitive overload driving compassion fatigue in learners, how AI can support sustainable feedback practices, and why the hidden curriculum in medical training might be one of the places AI has the most to offer. Whether you work in the clinic, the classroom, or the research lab, Nia's perspective on AI and professional identity is one I think will stay with you.
Transcribe →Your Documents, Your AI: A Practical Guide to NotebookLM
Apr 800:25:52Tap to summarizeNot 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 overviews, infographics, and slide decks — and I spend real time on use cases for educators, a group I think has been underserved in some of our earlier conversations. We also get into the privacy and data governance considerations that anyone in academic medicine needs to understand before uploading anything to a cloud-based AI tool. If you've been curious about NotebookLM but weren't sure where it fits in your work, or whether it's appropriate for your context, this episode is your starting point. This episode also serves as an early introduction to Retrieval-Augmented Generation — a concept we'll return to in depth later this season.https://notebooklm.google
Transcribe →What You Gain and What You Lose: A Risk/Benefit Framework for AI Use
Mar 2500:16:53Tap to summarizeAI 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 versus agency, and convenience versus privacy. Using the same risk/benefit framework you apply to clinical decisions, I'll help you evaluate when AI use makes sense and when the trade-off isn't worth it.Understanding these fundamentals — especially around agency and privacy — becomes critical as AI takes on more autonomous roles. You're already skilled at risk/benefit analysis. You just need to apply it to a new domain.https://www.media.mit.edu/publications/your-brain-on-chatgpt/
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