2 Jul 2026 10:00
242: How to Teach AI to Healthcare Professionals | Podcast with Candice Chu
What does AI literacy actually look like for pathologists, researchers, and future clinicians? And how do you teach it in a way that is practical, not abstract?
In this episode, I talk with Candice Chu, DVM, PhD about something I think a lot of people in digital pathology and computational pathology are feeling right now: AI is moving fast, but education is still catching up.
Candice is a clinical pathologist, veterinarian, and educator building AI-focused teaching and research at Texas A&M. We worked together before on digital pathology and image analysis projects, so this conversation felt especially grounded. We talk about her AI literacy curriculum framework for veterinary education, why she decided to build it, and what it takes to teach AI in a way that is useful, ethical, and realistic.
This episode is about understanding what AI tools are good for, where they can waste your time, and why hands-on experience matters. Candice explains why she sees AI as a set of tools, not a belief system. Try them. Learn them. Keep what improves your workflow. Drop what does not.
We also talk about the difference between putting educational content online and building formal institutional teaching. That matters because social media can move quickly, but curriculum changes, research, and professional organizations shape longer-term adoption. Candice shares how her course started as a low-stakes elective, then grew into a more structured framework that combines education with publishable research.
A big part of this conversation is the curriculum itself. We go through what students actually learn: AI fundamentals without heavy math, machine learning and image analysis, large language models, prompt engineering, chatbot building, ethics, literature research, and final projects where students evaluate real tools and workflows. I liked that the course does not stop at theory. It asks students to use tools, question them, and explain where they help and where they do not.
We also get into something that matters far beyond veterinary medicine: professional responsibility. If AI is involved in a workflow, the clinician is still responsible. That includes fabricated citations, bad outputs, weak prompts, and the temptation to trust tools too quickly. Candice makes a strong case that AI education needs ethics, legal context, and interdisciplinary teaching built in from the start.
If you are trying to think more clearly about AI in pathology, education, workflow design, or professional training, this episode gives you a concrete example of what responsible AI literacy can look like.
Episode Highlights
00:00 – Why AI tools are just tools, and why trying them matters even if you later decide not to keep using them
00:33 – Who Candice Chu is and why her work on AI literacy in veterinary medicine is worth paying attention to
02:33 – Why going back to Texas A&M changed the scale of Candice’s AI research and teaching
07:53 – How the AI course was designed as a low-stakes elective first, and why that helped student engagement
11:16 – Where veterinary AI education stands now, and what professional organizations like ACVP are doing
13:08 – Why AI adoption in veterinary medicine is still slow, and what skepticism usually sounds like in practice
15:19 – Real examples of how Candice uses LLMs and computer vision in pathology, medical records, and research
19:58 – What is actually inside the 15-week AI literacy curriculum, from fundamentals to final projects
24:16 – Why ethics and legal responsibility are not optional in AI education
31:35 – Why no-code tools and vibe coding are entering the curriculum already
38:50 – The AI tools Candice is testing in her own workflow, including Claude, Codex, and Perplexity
