Preparing healthcare leaders of the digital age with an integrative artificial intelligence curriculum: a pilot study.

Journal: Medical education online
Published Date:

Abstract

Artificial intelligence (AI) is rapidly being introduced into the clinical workflow of many specialties. Despite the need to train physicians who understand the utility and implications of AI and mitigate a growing skills gap, no established consensus exists on how to best introduce AI concepts to medical students during preclinical training. This study examined the effectiveness of a pilot Digital Health Scholars (DHS) non-credit enrichment elective that paralleled the Dartmouth Geisel School of Medicine's first-year preclinical curriculum with a focus on introducing AI algorithms and their applications in the concurrently occurring systems-blocks. From September 2022 to March 2023, ten self-selected first-year students enrolled in the elective curriculum run in parallel with four existing curricular blocks (Immunology, Hematology, Cardiology, and Pulmonology). Each DHS block consisted of a journal club, a live-coding demonstration, and an integration session led by a researcher in that field. Students' confidence in explaining the content objectives (high-level knowledge, implications, and limitations of AI) was measured before and after each block and compared using Mann-Whitney tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology:  = 4.5,  = 0.030; Hematology:  = 1.0,  = 0.009; Cardiology:  = 4.0,  = 0.019; Pulmonology:  = 4.0,  = 0.030) as well as an average overall satisfaction level of 4.29/5 in rating the curriculum content. Our study demonstrates that a digital health enrichment elective that runs in parallel to an institution's preclinical curriculum and embeds AI concepts into relevant clinical topics can enhance students' confidence in describing the content objectives that pertain to high-level algorithmic understanding, implications, and limitations of the studied models. Building on this elective curricular design, further studies with a larger enrollment can help determine the most effective approach in preparing future physicians for the AI-enhanced clinical workflow.

Authors

  • Soo Hwan Park
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Roshini Pinto-Powell
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Thomas Thesen
    Comprehensive Epilepsy Center, Department of Neurology, School of Medicine, New York University, New York, USA; Department of Radiology, School of Medicine, New York University, New York, USA. Electronic address: thomas.thesen@med.nyu.edu.
  • Alexander Lindqwister
    Department of Radiology, Stanford Medicine, Palo Alto, CA, USA.
  • Joshua Levy
    Department of Pathology & Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
  • Rachael Chacko
    Geisel School of Medicine, Hanover, New Hampshire, USA.
  • Devina Gonzalez
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Connor Bridges
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Adam Schwendt
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Travis Byrum
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Justin Fong
  • Shahin Shasavari
    Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Saeed Hassanpour
    Lia Harrington, Todd MacKenzie, and Saeed Hassanpour, Geisel School of Medicine at Dartmouth College, Hanover; Roberta diFlorio-Alexander, Katherine Trinh, and Arief Suriawinata, Dartmouth-Hitchcock Medical Center, Lebanon, NH.