Grounded in reality: artificial intelligence in medical education
Open accessAbstract
Background: In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum. Methods: We applied constructivist and backwards design principles to design online learning assignments simulating the real-world work done in the healthcare industry. Our innovative approach assumed no technical background for students, yet addressed the need for training clinicians to be ready to practice in the new digital patient care environment. This modular 4-week AI course was implemented in 2019, integrating AI with evidence-based medicine, pathology, pharmacology, tele-monitoring, quality improvement, value-based care, and patient safety. Results: This educational innovation was tested in 2 cohorts of fourth year medical students who demonstrated an improvement in knowledge with an average quiz score of 97% and in skills with an average application assignment score of 89%. Weekly reflections revealed how students learned to transition from theory to practice of AI and how these concepts might apply to their upcoming residency training programs and future medical practice. Conclusions: We present an innovative product that achieves the objective of competency-based education of students regarding the role of AI in medicine. This course can be integrated in the preclinical years with a focus on foundational knowledge, vocabulary, and concepts, and in clinical years with a focus on application of core knowledge to real-world scenarios.
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Krive et al. created a modular 4-week AI elective for fourth-year medical students, primarily delivered online, aligning with our elements of critical appraisal of AI research/technology (A7, A13), clinical interpretation of results from AI tools (A11), developing strategies to mitigate bias (E8), and communicating results to patients (COM3).
“Krive et al. Created a modular 4-week AI elective for fourth-year medical students, primarily delivered online, aligning with our elements of critical appraisal of AI research/technology (A7, A13), clinical interpretation of results from AI tools (A11), developing strategies to mitigate bias (E8), and communicating results to patients (COM3)20.”
From Developing a Canadian artificial intelligence medical curriculum using a Delphi study (2024) · cites this paper as [20]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredStudents exposed to GenAI-generated cases exhibit improved clinical reasoning compared to those trained through traditional methods.
“Students exposed to GenAI-generated cases exhibit improved clinical reasoning compared to those trained through traditional methods [19].”
From Effects of generative artificial intelligence (GenAI) patient simulation on perceived clinical competency among global nursing undergraduates: a cross-over randomised controlled trial (2025) · cites this paper as [19]
The Methods (Design; page unavailable) describe assignments based on simulated patient-care scenarios, and the Abstract (Results; page unavailable) reports improved AI knowledge and skills. Neither passage says the cases were GenAI-generated or reports a comparison with traditional training on clinical reasoning. Searches for GenAI-generated cases and a traditional-methods comparison found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredUnderstanding AI requires training, especially its impact on care management decisions, patient outcomes discussions, and ethical decision-making.
“Understanding AI requires training, especially its impact on care management decisions, patient outcomes discussions, and ethical decision-making [2,39,40].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [39]
There is increasing agreement within the literature on the need for health informatics skills, as shown in multiple recently published papers suggesting various educational methods and frameworks.
“There is increasing agreement within the literature on this need as shown in multiple recently published papers suggesting various educational methods and frameworks to accomplish this [39,44].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [39]
There have been some recent attempts to create frameworks for medical school implementation of AI technologies.
“There have been some recent attempts to create frameworks for medical school implementation of AI technologies.”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [39]
Krive et al created an AI-driven medical education module concentrating on three competencies: students as evaluators through critical appraisal of AI systems, students as interpreters of AI output, and students as communicators of AI results and processes.
“Krive et al [39] created an AI-driven medical education module, concentrating on 3 competencies: students as evaluators through critical appraisal of AI systems, students as interpreters of AI output, and students as communicators of AI results and processes.”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [39]
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- Authors
- Krive, J., Isola, M., Chang, L., Patel, T., Anderson, M., & Sreedhar, R.
- Affiliations
- University of Illinois Chicago (US)
- University of Illinois Chicago, Rockford campus (US)
- Corresponding author
- Krive, Jacob
- Venue
- JAMIA Open
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-04-06
- Volume
- 6
- Issue
- 2
- Pages
- ooad037-ooad037
- ISSN
- 2574-2531 (ISSN-L)
- PMC
- PMC10234762
- OpenAlex
- W4379185283
- Cited by
- 93
- References
- 12
- Citations by year
- 2026: 31 · 2025: 35 · 2024: 21 · 2023: 6
- FWCI
- 3.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JAMIA Open (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- University of Illinois at Urbana-Champaign
- University of Illinois at Chicago
- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence in healthcare, medical education, AI competencies, undergraduate medical curriculum, competency-based education, backward design, constructivist learning, remote patient monitoring, value-based care, patient safety, quality improvement, evidence-based medicine
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