“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 author

 

The topic of medical students and artificial intelligence education is the central focus of a timely commentary authored by faculty members of University of Toronto and MIT, including our colleague and friend Leo Celi, who is one of the progenitors of MIT-sponsored datathons.

The authors advocate for a dual-focused approach: combining robust, learner-centered AI additions to baseline curricula and extracurricular programs to cultivate leadership in this space. Questions raised and answered in this commentary include: What do physicians need to understand about AI in the clinical context and in the broader professional context? These questions are obviously not solely for the medical students but trainees, young and senior faculty, and other healthcare professionals as well.

The basic answer is that the physicians need to understand AI in the same way that they need to understand any technology impacting clinical decision-making. The “black box” effect of AI should not be a rationalization for foregoing the explainability of AI. The authors further suggest that the education include important dimensions of artificial intelligence such as explainability, health equity, and data security, but other dimensions such as digital health, data and databases, and entrepreneurship could also be included in this portfolio of future-oriented medical education.

A detailed table in this commentary of potential curricular and extracurricular learning opportunities for artificial intelligence in medicine that focuses on a learner-centered ethos can be useful for any medical school educator who espouses student engagement rather than passive knowledge transfer.

Finally, the authors recommend “a multidisciplinary and integrated approach” as well as “concerted efforts to cultivate physician leaders who are fluent in both AI and medicine” to “select clinically relevant and computationally feasible targets for AI in medicine.”

The most important caveat is not to treat artificial intelligence as only a novel technology, but along with healthcare data and digital medicine, an essential portfolio in the paradigm shift in clinical medicine and health care for the future clinician to accommodate and indoctrinate. The tenets of randomized controlled trials and evidence-based medicine can and should remain for certain subareas of investigation, but data science-driven approaches and “intelligence-based medicine” need to become an increasingly important part of the research and innovation milieu to both answer old queries and ask new questions as well as daily clinical practice for all clinicians and stakeholders in healthcare.

It is finally time to stop the top-down education of the older era and teach our young clinicians to yearn for the vast and endless sea of clinical medicine and health care.

 

The full article can be read here