PurposeArtificial intelligence (AI) is a rapidly growing phenomenon poised to instigate large-scale changes in medicine. However, medical education has not kept pace with the rapid advancements of AI. Despite several calls to action, the adoption of teaching on AI in undergraduate medical education (UME) has been limited. This scoping review aims to identify gaps and key themes in the peer-reviewed literature on AI training in UME. MethodThe scoping review was informed by Arksey and O'Malley's methodology. Seven electronic databases including MEDLINE and EMBASE were searched for articles discussing the inclusion of AI in UME between January 2000 and July 2020. A total of 4,299 articles were independently screened by 3 co-investigators and 22 full-text articles were included. Data were extracted using a standardized checklist. Themes were identified using iterative thematic analysis.

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Letters to the Editor who were admitted with both pneumonia and asthma typically went straight to the intensive care unit, hence experienced fewer complications, but there was no way of including this contextual information in the AI's algorithm.Given the complexity of AI algorithms and their inability to deal with the context of patient care (at least for the foreseeable future), it is hard to see how AI tools can operate transparently. It is also difficult to imagine that such AI systems could replace human clinicians any more than a Google search could.

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