Dr. Anthony Chang

“When I think back on my own best teachers, I generally don’t remember what was on the curriculum, but rather who they were. Whether the subject of the course was in the sciences or in the humanities, I remember how these teachers modeled a passion for knowledge, a funny and dynamic way of connecting with students. They also modeled a set of moral virtues — how to be rigorous with evidence, how to admit error, how to coach students as they make their own discoveries. I remember how I admired them and wanted to be like them. That’s a kind of knowledge you’ll never get from a bot.”

                                                                                                                                                                                    David Brooks, New York Times editorial

 

With the exponential rise of artificial intelligence technology, the absolute need for substantive education of topics in data science and artificial intelligence for our future health professionals is more than ever before. My experience with teaching artificial intelligence in several health professional schools both here in the US and abroad this past decade has been both rewarding as well as insightful. Here are several important takeaway lessons learned from these teaching experiences with a myriad of health professional schools and from feedback and input from the students and faculty:

1. Involve local faculty and students from the beginning for interdisciplinary team-based, collaborative teaching and learning - Artificial intelligence as a domain is foreign not only to the students, but also to most, if not all, of the faculty. To smoothly incorporate artificial intelligence and its content into the health professional school curriculum, the overall process would undergo three stages: design, construction, and implementation. Both the faculty (clinical and data science) and the students were always involved at the early design stage and onward, and the faculty members were always encouraged to be part of the AI classes. I usually inaugurated each school’s AI program with an in-person orientation session of 2-4 hours, and the subsequent AI classes ranged from weekly to monthly depending on the school. This interdisciplinary (or transdisciplinary) strategy promotes collaborative teaching and learning for everyone resulting in a special synergy and mutual respect and appreciation.
2. Focus not on the programming but rather on concepts of artificial intelligence that are relatable and relevant - most clinicians prefer not to be deeply involved in learning how to program in languages like Python, Matlab, or R. Especially with automated tools like the low-code/no-code large language models and autoML platforms, the absolute need to learn to program is less than ever before. I have learned to “contextualize” the AI concepts into the curricular content so the AI content is introduced in a way that is both relatable and relevant. For example, if the students have just learned about diabetes, I would introduce both the AI concepts of convolutional neural networks (deep learning) in funduscopic photograph interpretation as well as AI-enabled diabetes care with wearable devices. This contextual strategy of immediate relevance and retention engages and motivates both the students as well as the faculty members to attain critical thinking in the era of AI.
3. Encourage students and faculty to use their AI knowledge in problem-based, clinically integrated learning - the students and faculty are asked to present current hot topics in AI and AI in healthcare (recent discussions included consequences of DeepSeek and Manus on AI in healthcare). In addition, the advent of large language models is an effective inspiration to have students both understand and use these AI tools. Lastly, the students and faculty can be encouraged to think about how AI can be used with case-based learning. For instance, there can be an open discussion about how AI can be used in real-world aspects of a cancer patient from diagnosis onward (CNN for imaging, data science for identifying the correct clinical trial, agentic AI for followup care, etc) and even a hands-on experiential AI project. This real-world strategy of problem-based learning encourages students and faculty to demystify AI as a supportive clinical tool rather than a theoretical abstraction (“black box”).

I like to express my deepest gratitude to the many students and faculty members that I had the distinct pleasure of collaborating with for their patience and fortitude. I like to thank especially my very brave future-facing innovative partners from their schools: Dr. David Cheng of the Chinese University of Hong Kong-Shenzhen, Dr. Rubin Pillay of University of Alabama Heersink School of Medicine, Dr. Amy Bronson of the Master of Physician Assistant program at West Coast University, Dr. Jeff Gold of University of Nebraska Medical Center, and Dr. David Ninan of Kansas College of Osteopathic Medicine as well as Ms. Rebecca Wiedemer, Ms. Pearl Xu, and Drs. Arlen Meyers and Sharief Taraman from Medical Intelligence 10 (MI10) for their unwavering support for my efforts to help create a needed interface between clinical sciences and artificial intelligence and to transform health professional education. Most of these medical education pioneers supporting AI will be part of the faculty at the special Medical Education of AI track at the upcoming AIMed25 meeting in San Diego later this year. 

AIMed25 is the longest running meeting focused on artificial intelligence in medicine and healthcare for clinicians, healthcare leaders, data scientists, entrepreneurs, and innovators who are shaping the next era of healthcare with artificial intelligence.

Across three action-packed days, experience:

• Breakthrough discussions on Generative AI, Agentic AI, Large Language Models, and iXR

• Specialized tracks on Pediatrics & Neonatology, AI in Medical Education, and AI & Mental Health

• Hot topic breakfast workshops — you choose the conversations

• 20+ clinical subspecialty breakouts

• Scholarship-winning abstract competitions

• The prestigious ABAIM certification course

• Plus, a brand-new Chief AI Officer agenda for executive leaders.

AIMed is more than a conference - it's a community, a movement and a catalyst for change. Secure your ticket today and be part of the future of healthcare.