A smarter perspective: Learning with and from AI-cases
Open accessAbstract
Artificial intelligence (AI) has only partially (or not at all) been integrated into medical education, leading to growing concerns regarding how to train healthcare practitioners to handle the changes brought about by the introduction of AI. Programming lessons and other technical information into healthcare curricula has been proposed as a solution to support healthcare personnel in using AI or other future technology. However, integrating these core elements of computer science knowledge might not meet the observed need that students will benefit from gaining practical experience with AI in the direct application area. Therefore, this paper proposes a dynamic approach to case-based learning that utilizes the scenarios where AI is currently used in clinical practice as examples. This approach will support students' understanding of technical aspects. Case-based learning with AI as an example provides additional benefits: (1) it allows doctors to compare their thought processes to the AI suggestions and critically reflect on the assumptions and biases of AI and clinical practice; (2) it incentivizes doctors to discuss and address ethical issues inherent to technology and those already existing in current clinical practice; (3) it serves as a foundation for fostering interdisciplinary collaboration via discussion of different views between technologists, multidisciplinary experts, and healthcare professionals. The proposed knowledge shift from AI as a technical focus to AI as an example for case-based learning aims to encourage a different perspective on educational needs. Technical education does not need to compete with other essential clinical skills as it could serve as a basis for supporting them, which leads to better medical education and practice, ultimately benefiting patients.
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In one study, students were provided with AI-generated scenarios, and educators compared students' thought processes with AI-generated suggestions; the approach facilitated critical reflections.
“In one, students were provided with AI-generated scenarios, and educators compared students’ thought processes with AI-generated suggestions [150]. The approach facilitated critical reflections.”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [150]
The Introduction describes a proposed, theoretically informed approach using existing clinical AI scenarios as examples for students, not a study in which students were given AI-generated scenarios. The Abstract and §4.1 say doctors could compare their thinking with AI suggestions and critically reflect; they do not report educators making that comparison or an observed improvement in reflection. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThree additional studies explored the integration of AI in case-based learning.
“Three additional studies explored the integration of AI in case-based learning [150–152].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [150]
Uncertainty about AI's real-world applicability in medicine was described as a challenge.
“uncertainty about AI’s real-world applicability in medicine [150]”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [150]
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- Authors
- Arbelaez Ossa, L., Rost, M., Lorenzini, G., Shaw, D. M., & Elger, B. S.
- Affiliations
- University of Basel (CH)
- University of Geneva (CH)
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 135
- Pages
- 102458
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W4309729793
- Cited by
- 39
- References
- 37
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 10 · 2023: 6
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- SERVAL (Université de Lausanne) (submittedVersion, cc-by)
- IRIS (submittedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (187263, 407740_187263 /1)
- 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
- Social Sciences › Education › Problem and Project Based Learning
- Keywords
- artificial intelligence in medical education, case-based learning, clinical reasoning, interdisciplinary collaboration, clinical skills
- MeSH terms
- Artificial Intelligence, Education, Medical, Health Personnel, Humans, Learning, Physicians
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