Two Year Experience of a Virtual, Self-Guided Artificial Intelligence Curriculum for Fourth-Year Medical Students (Preprint)

Abstract

Artificial intelligence (AI) and machine learning (ML) are poised to have a significant impact in the healthcare space. While a plethora of online resources exist to teach programming skills and machine learning model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty.

The authors theorized that a 1-month elective for fourth year medical students, composed of high-quality existing online resources and a project-based structure, would empower students to learn about the impact of AI/ML in their chosen specialty and begin contributing to innovation in their field of interest. In this paper, we share our two year experience and publish our curriculum for other educators who may be interested in its adoption.

This elective was offered in two tracks: Technical (for students who were already competent programmers) and Non-Technical (with no technical prerequisites, focusing on building a conceptual understanding of AI/ML). Students established a conceptual foundation of knowledge using curated online resources and relevant research papers, and were then tasked with completing three projects in their chosen specialty: a dataset analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student’s interest area and career goals. Students’ success was measured by self-reported confidence in AI/ML skills in pre- and post-surveys. Qualitative feedback on students’ experiences were also collected.

This virtual, self-directed elective was offered on a pass/fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, nineteen students had successfully completed the elective, representing a wide range of chosen specialties: Diagnostic Radiology (n=3), General Surgery (1), Internal Medicine (5), Neurology (2), Obstetrics/Gynecology (1), Ophthalmology (1), Orthopedic Surgery (1), Otolaryngology (2), Pathology (2), and Pediatrics (1). Students’ self-reported confidence scores for AI/ML rose by 66% after this one-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course, and commented that the self-direction and flexibility and the project-based design of the course were essential.

Course participants were successful in diving deep into applications of AI/ML in their widely ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, one-month investment in AI/ML education during medical school will empower this next generation of physicians to pave the way for AI/ML innovation in healthcare.