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

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 U tests. Students reported significant increases in confidence in describing the content objectives after all four blocks (Immunology: U = 4.5, p = 0.030; Hematology: U = 1.0, p = 0.009; Cardiology: U = 4.0, p = 0.019; Pulmonology: U = 4.0, p = 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.

KEYWORDS:

• Artificial intelligence
• machine learning
• data science
• undergraduate medical education
• preclinical training

Acknowledgments

The authors would like to thank the invited speakers, Shiwei Xu, Jessilyn Dunn PhD, Jeremiah Brown PhD, and Liesbeth Hondelink MD, for their contributions to reviewing the data science concepts and leading the integrative discussions. The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics approval

This is a medical education study. Exemption from ethics approval was granted by Dartmouth College’s Committee for Protection of Human Rights on 13 June 2022 (#STUDY00032539).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10872981.2024.2315684.

Additional information

Funding

The Article Processing Charge has been supported by the Dartmouth Library’s Open Access Publishing Fund.