AI Rounds by the Cumming School of Medicine

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 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?

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 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

Delegating 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.

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 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.

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 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.

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 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

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 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/

This brief episode wraps up Season 1 of AI Rounds. Thank you, listeners, for tuning in, we've covered a lot of ground together — from how AI models learn to practical applications in teaching, research, and clinical work. Season 2 will launch in Spring 2026, and I'd love your help shaping upcoming content. If you have topic suggestions, please send them to [email protected].

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 frameworks for deciding when AI helps versus hinders their learning, cultivating critical thinking skills that will serve them throughout their research careers, and unexpected ways GenAI is impacting graduate research.Join us for a nuanced and thought provoking conversation that touches on several themes we've seen throughout this season.

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 research literature, track administrative deadlines, support educational workflows, and enhance clinical decision-making.In this episode we break down what AI agents actually are, explores types most relevant to clinical practice and medical education, and provides a practical framework for building your first agent using no-code platforms. The episode covers essential considerations for medical environments, including privacy, security, and integration with existing systems.Links from this episode:zapier.comifttt.commicrosoft.com/en-us/power-platform/products/power-automate

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 personalization. And each time, education evolves—not by replacing human connection, but by thoughtfully integrating new tools into teaching practice.D'Arcy shares insights from his work leading learning technology initiatives at the University of Calgary, offering medical educators a practical perspective for evaluating AI tools while preserving what matters most: the relationships, mentorship, and clinical judgment that form the core of medical training.If you're a medical faculty member navigating AI fears, wondering how to maintain academic integrity, or simply trying to understand where AI fits in your teaching, this episode provides both historical perspective and actionable guidance. The message is clear: teachers won't be replaced by AI, and student learning won't be diminished—provided we embrace intentional course design and authentic assessment.The wave will pass. The question is how we ride it.

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 saturation (Episode 9), and explains why the same evaluation methods that create impressive test scores actually reward confident guessing over appropriate uncertainty. For medical faculty evaluating AI tools, understanding these statistical realities is crucial for teaching students, conducting research, and developing institutional policies that account for AI's fundamental limitations.Links from this episode:https://openai.com/index/why-language-models-hallucinate

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 insights on navigating uncharted territory. We explore the faculty hesitations to embracing AI, reimagine what authentic assessment looks like when AI can write and analyze, and dive deep into the ethical minefields emerging in AI-assisted research.But here's the kicker – in a mic-drop moment that will make you question everything, Heather poses the ultimate challenge: "Should we even examine a written thesis anymore?" This isn't just about adapting to new tools; it's about fundamentally rethinking what graduate education means in an AI-ubiquitous world.Whether you're supervising students, designing curricula, or simply trying to keep pace with the AI revolution in academia, this episode will challenge your assumptions and equip you with practical thoughts for the road ahead. The future of graduate education isn't coming – it's here.Join us for a conversation that's equal parts challenging and inspiring, as we explore how to prepare medical graduates not just to use AI, but to lead its ethical implementation in research and clinical practice.

Our institution now has campus-wide access to scite.ai, an AI-powered research tool that's fundamentally different from traditional databases. In this episode, we explore how medical faculty can leverage this powerful platform to transform their research workflows, enhance teaching, and support evidence-based clinical decisions. Whether you're writing your next grant, preparing tomorrow's lecture, or answering a resident's question during rounds, this episode provides concrete strategies to work smarter, not harder with scite!Links from this episode:scite can be accessed at https://scite.aiFor UCalgary users if you are on the UCalgary network you will automatically have full access to all features, no log in required. From outside of the network you an log in via the UCalgary library ezproxy. No account is required but if you make one you will have the advantage of being able to save your work.

In 2025, artificial intelligence has achieved an unexpected milestone: it's become too good at taking tests. From medical knowledge exams to complex reasoning tasks, AI systems are now scoring 90%+ on benchmarks that were designed to challenge them, rendering these assessments meaningless for comparison or evaluation. This "benchmark crisis" has profound implications for medical faculty evaluating AI tools for research, education, and clinical applications. When vendors claim their AI scored "95% on medical benchmarks," what does that actually tell us about real-world performance? This episode explores why perfect scores might be misleading, how the benchmark arms race mirrors challenges in medical education assessment, and what questions faculty should ask when evaluating AI tools for their institutions. Understanding this crisis is crucial for making informed decisions about AI integration in academic medicine.

The Game Has Changed! Forget everything you know about AI "search." In the past few months GenAI tools have released "deep research" capabilities which can spend up to 45 minutes autonomously investigating complex medical questions, synthesize findings from hundreds of papers in minutes, and generate comprehensive literature reviews that used to take weeks. See how you can save hours per week while maintaining rigorous academic standards. But can you trust the results? How do you verify AI-generated citations? And what does this mean for research integrity in academic medicine?

This week Jessalyn sits down with Dr. Jordan Engbers, who works with AI both in industry and academia. The conversation explores how our relationship to intellectual work could (and should!) change as we incorporate AI into our workflows. The discussion continues thinking about what we are teaching in higher education regarding AI use and what employers are looking for these days in industry related to AI skills.

This week Jessalyn has a fascinating conversation with guest Dr. Zack Marshall, an expert in Community-Based Participatory Research and Responsible AI. The conversation explores gender bias in AI, how to engage patients and the public in AI development, and transdisciplinary thinking and collaboration in AI. Check the links below for the projects mentioned in this episode.Shirin Seyedsalehi, Amin Bigdeli, Negar Arabzadeh, Batool AlMousawi, Zack Marshall, Morteza Zihayat and Ebrahim Bagheri (2025), "Understanding and Mitigating Gender Bias in Information Retrieval Systems", Foundations and Trends® in Information Retrieval: Vol. 19: No. 3, pp 191-364. (http://dx.doi.org/10.1561/1500000103)AbSPORuPaCERNotebookLMData Nutrition ProjectResponsible AI Project

On this episode, Jessalyn sits down with Dr. Nonsikelelo Mathe (Scientific Director, Health Equity & Implementation Science, Health Equity & Systems Transformation Lab, CSM) to discuss equity, ethics, and privacy when thinking about using AI in the healthcare system.

On this episode, we explore essential AI prompting techniques. Whether you're a health sciences professor, medical educator, or practicing clinician, you'll learn practical strategies to get better results from AI tools like ChatGPT, Claude, and Gemini in your professional work. We cover five core prompting principles and advanced techniques including Chain of Thought reasoning with examples.You'll also discover how to avoid common prompting pitfalls, understand machine psychology to leverage AI's information processing patterns, and explore Retrieval-Augmented Generation (RAG) technology for enhanced reliability in healthcare applications. Designed for busy healthcare professionals, this episode offers immediately actionable insights to help you save time, improve AI interactions, and get more clinically relevant outputs for your practice, teaching, and research.

On this episode, Jessalyn sits down with Dr. Fareen Zaver, Associate Dean, Office of Faculty Development to discuss why faculty members should prioritize AI literacy in their professional development.

Take a deep dive into large language models and generative AI like ChatGPT, Claude, or Gemini.On this episode, Jessalyn will dig into how these systems actually work, discuss their current capabilities and limitations, and share some practical tips for leveraging them effectively.

New to AI? No problem! On this episode, Jessalyn breaks down artificial intelligence using a powerful analogy: how children learn to understand their world. Just as kids start with basic concepts before tackling complex ones, you too can grasp AI fundamentals without a technical background.

AI Rounds is an educational podcast designed specifically 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 in your work and discussing how these technologies are reshaping medical research and education. Join us as we examine AI in medicine 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.https://cumming.ucalgary.ca/office/ofd