Grounded in reality: artificial intelligence in medical education

Lay Summary Despite headwinds in usability and outcomes, clinical analytics and artificial intelligence (AI) play an increasingly important role in medical practice. Medical professionals find themselves within a paradigm shift in the healthcare delivery models that rely on technology, yet AI remains a gap in standard medical education. It is generally perceived that medical students are academically unprepared to study technology topics in school and do not require technology skills in practice. Yet, students desire technology topics and identify AI among their knowledge needs. There is no development model or standard for analytics and AI curriculum in medical schools, while interest in the topic among students, faculty, and practitioners is growing. To address the gap in AI education and the challenge of working around mathematics and computer science preparation among medical students, we created and piloted new curriculum with 2 medical student cohorts, by focusing on the role of clinicians in the processes of analytics/AI innovation and practice in the digitally enabled workplace. This online curriculum assumes no prior exposure to computer science topics and fits into multiple modes of delivery to students. It received positive response from the pilot cohorts. We report on methods, content, and results of this academic endeavor. Background In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum. Methods We applied constructivist and backwards design principles to design online learning assignments simulating the real-world work done in the healthcare industry. Our innovative approach assumed no technical background for students, yet addressed the need for training clinicians to be ready to practice in the new digital patient care environment. This modular 4-week AI course was implemented in 2019, integrating AI with evidence-based medicine, pathology, pharmacology, tele-monitoring, quality improvement, value-based care, and patient safety. Results This educational innovation was tested in 2 cohorts of fourth year medical students who demonstrated an improvement in knowledge with an average quiz score of 97% and in skills with an average application assignment score of 89%. Weekly reflections revealed how students learned to transition from theory to practice of AI and how these concepts might apply to their upcoming residency training programs and future medical practice. Conclusions We present an innovative product that achieves the objective of competency-based education of students regarding the role of AI in medicine. This course can be integrated in the preclinical years with a focus on foundational knowledge, vocabulary, and concepts, and in clinical years with a focus on application of core knowledge to real-world scenarios.