Artificial intelligence (AI) is poised to help deliver precision medicine and health.1,2 The clinical and biomedical research communities are increasingly embracing this modality to develop tools for diagnosis and prediction as well as to improve delivery and effectiveness of healthcare. New breakthroughs are being developed in an unprecedented fashion and the developed ones have obtained regulatory approval and found their way into routine medical practice.3,4,5 Yet, the medical school curriculum as well as the graduate medical education and other teaching programs within academic hospitals across the United States and around the world have not yet come to grips with educating students and trainees on this emerging technology. Several expert opinions have pointed to the benefits and limitations associated with the use of ML in medicine,1,2,6,7,8,9,10 but the aspect related to formally educating the younger generation of medical professionals has not been openly discussed.

Despite so, there is a lack of direct access to relevant ML education for clinicians and biomedical researchers. Various factors attribute to the failure of ML to be integrated within undergraduate and graduate medical education training. At present, there is no accreditation requirements related to retain curricular hours in the present schema with the emerging biomedical knowledge and demands for new content segments. In the United States, assessment in undergraduate medical education, places great emphasis on the preparation of licensing exams and a recent competency focus on entrustable professional activities (EPA's), none of which involves AI. In addition, medical schools fall short of faculty expertise needed to teach this content which is mainly conducted in computer science, mathematics and engineering faculties.