Artificial Intelligence Teaching as Part of Medical Education: Qualitative Analysis of Expert Interviews
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Abstract
BACKGROUND: The use of artificial intelligence (AI) in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize health care, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context. OBJECTIVE: We aimed to explore the relevant knowledge and understanding of the subject of AI in medicine and specify curricula teaching content within medical education. METHODS: For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, or teaching in the field of AI in medicine for at least 5 years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data. RESULTS: The qualitative content analysis led to the formation of three main categories ("Knowledge," "Interpretation," and "Application") with a total of 9 associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one's related competencies. CONCLUSIONS: The analyzed expert interviews' results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.
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The use of artificial intelligence in medicine is expected to have a significant impact on patient care, medical research, and health systems.
“The use of artificial intelligence (AI) in medicine is expected to have a significant impact on patient care, medical research, and health systems (Weidener and Fischer 2023).”
From A scoping review of artificial intelligence in medical education: BEME Guide No. 84 (2024) · cites this paper as Weidener and Fischer 2023
The Introduction (Background; page unavailable) says AI is expected to revolutionize health care systems and improve care for patients. The Introduction (Defining AI; page unavailable) gives examples of its use in patient care. The paper describes substantial scientific interest in AI, but does not say that AI is expected to have a significant impact on medical research. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFifteen publications focused explicitly on ethical considerations concerning the use of AI in medical education.
“15 publications focused explicitly on this topic [7, 38, 84, 97, 186, 190–199].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [38]
The Results section (“Knowledge,” subcategory “Ethics”; page unavailable) explicitly discusses ethical competencies for using AI in medicine. But the Conclusions describe a broader study of AI knowledge and skills for medical education, not a publication focused specifically on ethics. This paper also cannot establish the citing document’s count of 15 publications. (LLM, conf=0.85)
Experimental — accuracy not yet measuredA few studies employed either qualitative methods or a mixed-methods approach.
“In contrast, a few of them employed either qualitative methods [38–40] or a mixed-methods approach [41–45].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [38]
A lack of standard definitions for AI was described as a challenge to integrating AI in UGME curricula.
“These included a lack of standard definitions for AI [38, 39]”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [38]
Understanding 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 [2]
Weidener et al evaluated German medical students and concluded that students must possess fundamental knowledge of AI systems, the ability to interpret AI input and output, and comprehension of the appropriate application of AI systems.
“Weidener et al [2] evaluated German medical students and concluded that students must possess fundamental knowledge of AI systems, the ability to interpret AI input and output, and comprehension of the appropriate application of AI systems.”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [2]
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 [2]
Research recommends teaching AI ethics early in medical education to prepare for its potential impacts and challenges.
“Research recommends teaching AI ethics early in medical education to prepare for its potential impacts and challenges [6-8].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [7]
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- Authors
- Weidener, L., & Fischer, M.
- Affiliations
- UMIT TIROL - Private University for Health Sciences and Health Technology (AT)
- Corresponding author
- Weidener, Lukas
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2023-04-24
- Volume
- 9
- Pages
- e46428
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC10167581
- OpenAlex
- W4353016839
- Cited by
- 70
- References
- 36
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 21 · 2023: 7
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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, medical curriculum, medical students, AI ethics, technological skills, expert opinion
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