Top 5 Takeaways for Med Students to be AI Ready
by: Jerry Ding, Omnachmani and Puneet Seth
It’s virtually impossible to escape the chatter about the seismic role that AI will play in transforming nearly every modern industry, and healthcare is no exception. At the center of this revolution in healthcare are powerful language models like GPT-4 from OpenAI, Med-PALM 2 by Google, and HealthScribe by Amazon. These tools are rapidly becoming more proficient at interpreting lab results, reviewing medical literature, formulating differential diagnoses, recommending evidence-based treatment plans, and drafting radiology reports. It’s an exciting time, but it also leaves those of us in the training stages of our medical career wondering:
What’s the future for medical professionals? What skills will be crucial, and what new roles might we take on in this evolving landscape?
A recent article published in JMIR Medical Education outlined how medical schools can incorporate AI training as a core competency. However, this leaves those in training today wondering what can be done to be better prepared. In this article we explore 5 key takeaways for medical students and residents who are eager to become AI-ready in our evolving healthcare landscape:
- Educating Yourself on the Fundamentals of AI
Educate yourself on the fundamentals of AI using one of the many openly available online resources. Examples of great courses we recommend include the Google Cloud Generative AI Learning Path (free) and Stanford AI in Healthcare Specialization course on Coursera (paid).
2. Take advantage of opportunities within your school
Seek opportunities within your academic institution to expose yourself to AI in medicine. This may take the form of things such as elective courses, interest groups, guest seminars or research projects. An example of such an interest group is the AI in Medicine Student Society (AiMMS) at the University of Toronto. If no such resources exist at your school, this may be the perfect opportunity to make a request to your program coordinator or start an interest group yourself.
3. Get hands-on experience with use of AI tools
Use AI tools (where appropriate and ensuring privacy regulations are met) to gain familiarity with their function and consider how they can improve current processes. Engage with peers, seniors and medical staff who are effectively utilizing them. There are countless examples of such tools and the list grows every day. Below we provide a list of a few of them in no particular order.
AutoScribe – AI-powered digital scribe assistant
Tali AI – medical dictation, ambient scribe and voice virtual assistant
Nabla Co-Pilot – AI-powered digital scribe
Suki – AI-powered clinical voice assistant
HippoAI – clinical decision support tool powered by clinician reviewed clinical guidelines (disclosure: ON, one of the authors, is the co-founder of this tool)
Glass AI – GPT-4 powered clinical decision support tool
Note: Needless to say, by listing the tools above we don’t claim any due diligence is done nor can we claim they are appropriate for use in your region. An assessment for the appropriateness for use should be done by independently by each of you – that’s part of the practice!
4. Keep yourself up-to-date
This space is rapidly evolving, so it’s crucial to do what you can to remain up-to-date with what’s happening. Some of our favorite resources include the following:
Newsletters:
Healthcare AI News
Doctor Penguin
Podcasts:
Medicine and the Machine
NEJM AI Grand Rounds
Academic Journals:
BMJ Health & Care Informatics
NEJM AI
Nature Digital Medicine
JMIR and JMIR AI
Thought leaders:
Dr. Eric Topol
Dr. Pranav Rajpurkar
The Medical Futurist (Dr. Berci Meskó)
Dr. Daniel Kraft
5. Consider building a career in medical AI
If there is deeper interest or a desire for a career at the advancing intersection of medicine and AI, consider research, entrepreneurship or post-graduate studies in the field. There is an explosion of research happening in the space and universities are rapidly releasing new graduate programs that can help you build knowledge and exposure to the space. Some interesting new graduate programs include the MScAC (Masters of Science in Applied Computing) in Artificial Intelligence in Healthcare at the University of Toronto, which is launching later this year, and the Masters of Data Science and Artificial Intelligence (MDSAI) at the University of Waterloo, for those seeking a more technically comprehensive program in computer science and mathematics.
Medicine is inherently a profession that requires a dedication to lifelong learning, and the incorporation of AI into medical practice is a perfect example of where this will be crucial.
In the face of rapid AI advancements, it’s key for medical students and residents to actively engage in their own AI education, seeking out opportunities both within and outside their institutions.
As the landscape of healthcare shifts, being AI-ready not only ensures relevance in the profession but also opens doors to innovative ways of enhancing patient care. Embrace this change, and let it guide the next steps of your medical journey.
If there is a resource that you feel strongly about in being invaluable for medical professionals in training, please feel free to add it in the comments section below!
