ArticleJMIR medical education2024
Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education.
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development of AI competencies within the medical curriculum.Frontiers in medicine · 2026Pooled it
- AI Perceptions, Professional Identity, and AI-Supported Clinical Decisions Among Medical Students and Clinicians: Cross-Sectional Survey and Quasi-Randomized Vignette Study.JMIR medical education · 2026Article
- Role-Differentiated AI Competencies and Curriculum Implications for Health Professions Education: Qualitative Study.JMIR medical education · 2026Article
- Use, Concerns, and Perspectives on AI in Health Care Among French Health Professionals and Students: Web-Based Cross-Sectional Survey.JMIR medical education · 2026Article
- AI Integration in Spanish Undergraduate Medical Education: National Cross-Sectional Study.JMIR medical education · 2026Article
- Medical students' and faculty members' perceptions and experiences of AI integration in health care practice and in medical curricula: A meta-ethnographic review.Medical education · 2026Review
- Knowledge, Attitudes, and Training Needs for AI in Primary Care: National Survey Study of Clinicians in the Veterans Health Administration.JMIR formative research · 2026Article
- AI Competency: Current State and Challenges.JMIR medical education · 2026Article
- Assessing the level of readiness for digital transformation in medicine: students' perspectives on the use of artificial intelligence in health : a cross-sectional study.BMC medical education · 2026Article
- When Assessment Theory Meets Generative AI: Reimagining SBA Design in Medical Education.Perspectives on medical education · 2026Article
- Artificial intelligence in undergraduate medical education: an updated scoping review.BMC medical education · 2025Article
- The Spinal Chordoma Patient Reported Outcome Survey (spCPROS): a patient-centered, disease-specific tool for assessing quality of life in spinal chordoma patients.Journal of neuro-oncology · 2025Article
- The Craniopharyngioma Quality of Life (C-QOL) Index: a preliminary disease-specific, patient-centered tool for assessing patient reported outcomes in adult craniopharyngioma patients.Journal of neuro-oncology · 2025Article
- Article
- The skull base chordoma patient reported outcome survey (sbCPROS): a patient-centered, disease-specific tool for assessing quality of life in chordoma patients.Journal of neuro-oncology · 2025Article
- AI Use or Non-use by First-Year Medical Students: A Qualitative Study of Perspectives, Usage, and Recommendations.MedEdPublish (2016) · 2025Article
- Adoption challenges to artificial intelligence literacy in public healthcare: an evidence based study in Saudi Arabia.Frontiers in public health · 2025Article
- Faculty perspectives on artificial intelligence's adoption in the health sciences education: a multicentre survey.Frontiers in medicine · 2025Article
- Navigating the integration of large language models in healthcare: challenges, opportunities, and implications under the EU AI Act.Journal of anesthesia, analgesia and critical care · 2024Article
- A Training Needs Analysis for AI and Generative AI in Medical Education: Perspectives of Faculty and Students.Journal of medical education and curricular developmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective: This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods: The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results: Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions: The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.