Establishing A Curriculum for Resident and Medical Student Education of Machine Learning in Healthcare

Despite the expanding role of machine learning (ML) in healthcare and patient expectations for clinicians to understand ML-based tools, few for-credit curricula exist specifically for neurosurgical trainees to learn basic principles and implications of ML for medical research and clinical practice. We implemented a novel, remotely delivered curriculum designed specifically to develop literacy in ML for neurosurgical trainees.A 4-week pilot medical elective was designed specifically for trainees to build literacy in basic ML concepts. Qualitative feedback from interested and enrolled students was collected to assess students' and trainees' reactions, learning and future application of course content.Despite 15 interested learners, only three medical students and one neurosurgical resident completed the course. Enrollment included students and trainees from 3 different institutions. All learners who completed the course found the lectures relevant to their future practice as clinicians and researchers and reported improved confidence in applying and understanding published literature applying ML techniques in healthcare. Barriers to ample enrollment and retention (e.g. balancing clinical responsibilities) were identified.This pilot demonstrated the interest, value, and feasibility of a remote elective to establish ML literacy; however, feedback to increase accessibility and flexibility of the course encouraged our team to implement changes. Future elective iterations will have a semi-annual, 2-week format, splitting lectures more clearly between theory (the method and its value) and application (coding instructions) and make lectures open-source pre-requisites to allow tailoring of student learning to their planned application of these methods in their practice and research.