Implications of artificial intelligence for medical education

Abstract

Although digital health1Elenko E Underwood L Zohar D Defining digital medicine.Nat Biotechnol. 2015; 33: 456-461Crossref PubMed Scopus (99) Google Scholar has occasioned huge changes for medicine, the issues it provokes have yet to be integrated into teaching and learning across the medical education continuum. This question is all the more pressing given that the rise of artificial intelligence (AI) systems, discussed here as a specific example of healthcare's digitalisation, are associated with a fundamental paradigm shift in teaching. Whereas 20th-century medical education models relied on experimental results evolving into a recognised standard that then informed textbook teaching, today this sequencing no longer holds. The speed at which new health AI technologies are developing, being introduced into clinical practice, and being used by patients requires equipping doctors to deal appropriately with experimental techniques that have not yet become part of a generally accepted body of knowledge. Agile teaching and educated guesswork about which treatments will benefit patients the most are crucial for enabling physicians to lead the introduction of such technologies without simply being forced to react to them. Part of the task at hand is to ask how existing educational frameworks can be realistically updated to take into account 21st-century realities. As a rule, medical educators work with competency frameworks, of which several competing models exist, whereby a competence can be considered the suitable performance of several professional roles. Following Ellaway, we view such frameworks as theories outlining “a series of propositions and relationships that collectively define an ideal”, and therefore consider that they must be continuously tested and challenged.2Ellaway R CanMEDS is a theory.Adv Health Sci Educ Theory Pract. 2016; 21: 915-917Crossref Scopus (4) Google Scholar Today, the various abilities that physicians require to adequately meet patients' health-care needs are all affected by AI-enabled systems.3Topol EJ High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019; 25: 44-56Crossref PubMed Scopus (966) Google Scholar No one can predict the future ways in which technology will develop, but medicine serves common human needs, such as promoting patient well-being and making adequate health care available to all.4The Goals of MedicineHastings Cent Rep. 2016; 26: 7Google Scholar Meanwhile, we have a good picture of what patients want and need with regard to their own care, and how their preferences could be better integrated into medical education. As some patient advocates have written, this includes being considered full-value partners by medical educators, as well as “sensing that your doctor truly cares about what you are going through, and really does want to help”, and has the ability to “fully contextualise and appreciate the patient's values, wishes, and preferences”.5Mittelman M Markham S Taylor M Patient commentary: Stop hyping artificial intelligence-patients will always need human doctors.BMJ. 2018; 363k4669Crossref Scopus (7) Google Scholar As care has evolved to become more of a partnership, in which patients and their families have a key role to play in their treatment, physicians ought to collaborate with patients to develop and understand the patient's own relationship with AI and big data, which can vary dramatically. Moreover, they must work with patients from different backgrounds to develop sensitivities to problems of social justice and expert systems-driven solutions. By way of illustration, take one respected and widely used instrument, the Canadian Medical Education Directives for Specialists (CanMEDS) Physician Competency Framework, which has the advantage of being a practical and effective lever for change.6The Royal College of Physicians and Surgeons of CanadaCanMEDS Framework.http://www.royalcollege.ca/rcsite/canmeds/canmeds-framework-eDate acces