I am a pediatric cardiologist and have cared for children with heart disease for the past three decades. In addition, I have an educational background in business and finance as well as healthcare administration and global health – I gained a Masters Degree in Public Health from UCLA and taught Global Health there after I completed the program.
“If you want to build a ship, don’t drum up the men to gather wood, divide the work, and give orders. Instead, teach them to yearn for the vast and endless sea.”
Antoine de Saint-Exupery, French poet and pilot
Education of artificial intelligence in health professional schools has now engendered discussions more than ever. In the United States, more students are applying to medical schools than ever before, and yet only very few health professional schools (medical, nursing, pharmacy, dental, etc) are preparing their students for this upcoming paradigm in data science and artificial intelligence in healthcare.
This manuscript published in Nature Communications Medicine elucidates the artificial intelligence education experience in the Canadian medical school system in the form of a five-week “Introduction to Medical AI” workshop for medical students that was delivered three times between 2019-2021. The three learning objectives were:
- to understand how data is processed in an AI application
- to analyze clinical implications of AI literature
- to apply opportunities to collaborate with engineers in developing AI
Some of the early issues were somewhat predictable. Data scientists and computer scientists tend to be overly technical in their presentations and sometimes fail to bring clinical or medical relevance into their discussions. The four main challenges were:
- heterogeneity of prior knowledge
- attendance attrition
- curricular design
- knowledge retention
In addition, these workshops were very comprehensive and evolved into more clinical and practical sessions to accommodate the issues that may not be purely technical. The effort here should be applauded as much thought and planning was put in place and yet the team was very agile in making pivots to improve the curriculum.
My personal bias is that an introductory course of AI in Medicine should not focus on programming but rather on the myriad of essential issues and challenges as well as nuances and benefits of AI in medicine. At the very minimum, any education of programming should not be difficult enough to create a deterrent for medical students to learn about AI. In other words, medical students need to learn about appropriate applications of AI in medicine even before they learn how to debug a program. One of the best dividends of learning AI in medicine is that we healthcare professionals learn much about how we learn and practice healthcare.
Click here to read the full paper
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