A Model to Introduce Medical Students to the Use of Artificial Intelligence and Genomics for Precision Medicine
Open accessAbstract
Objective: Despite the significant medical impact of artificial intelligence (AI) in healthcare, emergence of AI-related topics in medical curricula has been slow. The authors sought to introduce pre-clinical students to the importance of AI methodologies and medical applications using modular short courses focused on active learning with precision medicine as a primary use case. Materials and Methods: A short elective course was designed to introduce first-year students to how various bioinformatic and AI-related processes work and how they help classify medical data, facilitate genomic analysis and predict clinical outcomes. The course covers gene sequencing and variants, neural networks, natural language processing, medical computer vision and the limitations and ethical concerns related to use of AI in precision medicine. Online content serves as major source material. After a faculty-led introduction, sessions focus on teams of students who present course content to one another and lead discussions with faculty guidance. A related short AI course focused on gene variants was given to the entire second-year class. Results: The elective course has been taken by 74 first- year students over 8 consecutive semesters (2017-2021). The course achieved average satisfaction scores of 4.4/5.0 (n = 13) when the active learning approach became dominant in 2018. Students were able to describe accurately how bioinformatics and AI make personalized medicine possible. Students also did well on the gene variants exercise given to the entire second year class (2018), but the full class short AI course was not continued in subsequent years. Students have created a school-approved interest group in medical AI. Conclusions: This experience shows that AI-related materials can be sustainably introduced into pre- clinical medical education with precision medicine as the primary use case. This modular course design and content could be adapted easily for educational use in medical subspecialties and other health professions.
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Limited space in already full curriculum was described as a challenge.
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From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [176]
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Artificial intelligence in undergraduate medical education: an updated scoping review
Simoni, J., Urtubia-Fernandez, J., Mengual, E., Simoni, D. A., Royo, M., Egaña-Yin, D., Hertog, O. L. A., López-Ortiz, L., Muñoz-Tomás, A., Santiago-Martínez, P., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08188-2 · Cited by 62 · see citing works
“These included a lack of standard definitions for AI [38, 39], no formal guidance from oversight bodies for UGME [163], a lack of consensus on core competencies [5, 174, 175], uncertainty about AI’s real-world applicability in medicine [150] and limited space in already full curriculum [176–178].”
- Authors
- Alderson, P. O., Donlin, M. J., & Morrison, L. A.
- Affiliations
- Saint Louis University (US)
- Corresponding author
- Alderson, Philip O.
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2021-05-17
- OpenAlex
- W3161181490
- Cited by
- 3
- References
- 27
- Citations by year
- 2025: 2 · 2024: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- medRxiv (acceptedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- artificial intelligence in healthcare, precision medicine, medical education, preclinical curriculum, active learning, elective course design, genomics education, genetic variants, bioinformatics, artificial neural networks, natural language processing, AI ethics, student satisfaction
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