This paper is in the following e-collection/theme issue:

Artificial Intelligence (AI) in Medical Education (182) e-Learning and Digital Medical Education (902) New Methods and Approaches in Medical Education (374) Health Professionals' Training in eHealth, Digital Medicine, Medical Informatics (186) User Needs and Competencies (456) Focus Groups and Qualitative Research for Human Factors Research (1220)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58355, first published .
Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education

Authors of this article:

Julia-Astrid Moldt1 Author Orcid Image ;   Teresa Festl-Wietek1 Author Orcid Image ;   Wolfgang Fuhl2, 3 Author Orcid Image ;   Susanne Zabel2, 3 Author Orcid Image ;   Manfred Claassen2, 3, 4 Author Orcid Image ;   Samuel Wagner5 Author Orcid Image ;   Kay Nieselt2, 3 Author Orcid Image ;   Anne Herrmann-Werner1, 6 Author Orcid Image
Article Authors Cited by (17) Tweetations (3) Metrics

Journals

  1. Li W, Shi H, Chen X, Lan J, Rehman A, Ge M, Shen L, Hu F, Jia Y, Li X, Chen H. Application of artificial intelligence in medical education: A meta-ethnographic synthesis. Medical Teacher 2025;47(7):1168 View
  2. Bignami E, Russo M, Lanza R, Bellini V. 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 2024;4(1) View
  3. Myneni S, Tang L, Akbari H, Mayne R, Ahmed A, Kazemi F, Dea N, Zwagerman N, Su S, Choby G, Wang E, Redmond K, McKean E, Snyderman C, Rowan N, Mukherjee D. 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 2025;173(1):157 View
  4. El-Sayed A, Alsenany S, Almalki R, Asal M. Fostering creativity-nurturing behaviors among nurse educators: Investigating the interplay between evidence-based practice climate and artificial intelligence competence self-efficacy. Nurse Education Today 2025;151:106734 View
  5. Nguyen K, Zillich A. Artificial intelligence is here, but are we ready?. JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2025;8(4):238 View
  6. Kumar R, Singh A, Kassar A, Humaida M, Joshi S, Sharma M. Adoption challenges to artificial intelligence literacy in public healthcare: an evidence based study in Saudi Arabia. Frontiers in Public Health 2025;13 View
  7. Moldt J, Festl-Wietek T, Fuhl W, Zabel S, Claassen M, Wagner S, Nieselt K, Herrmann-Werner A. Exploring the social dimensions of AI integration in healthcare: a qualitative study of stakeholder views on challenges and opportunities. BMJ Open 2025;15(6):e096208 View
  8. Salem M. A Digital Sustainability Lens: Investigating Medical Students’ Adoption Intentions for AI-Powered NLP Tools in Learning Environments. Sustainability 2025;17(14):6379 View
  9. Myneni S, Tang L, Akbari H, Mayne R, Kazemi F, Xiong R, Redmond K, Bilsky M, Charest-Morin R, Tobert D, Chakravarthy V, Shankar G, Lo S, Shin J, Wolinsky J, Sciubba D, Gokaslan Z, Dea N, Lubelski D, Ahmed A, Mukherjee D. 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 2025;175(2):813 View
  10. Faucher M, Sing E, Harris S, Hutson E, Hoelscher S. Artificial Intelligence and Competency‐Based Education: A Rapid Scoping Review. Journal of Midwifery & Women's Health 2025 View
  11. Al Zahrani E, Elsafi S, Al Musallam L, Alharbi A, Aldossari H, Alomar A, Alkharraz Z. Faculty perspectives on artificial intelligence’s adoption in the health sciences education: a multicentre survey. Frontiers in Medicine 2025;12 View
  12. Myneni S, Bhandarkar S, Akbari H, Mayne R, Ahmed A, Kazemi F, Almeida J, Germanwala A, Venteicher A, Zwagerman N, Wang E, Choby G, McKean E, Snyderman C, Rowan N, Mukherjee D. 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 2026;176(1) View
  13. Simoni J, Mengual E, Aschenbrenner-Noriega I, Monforte-Martínez C, Pérez-Merino M, Sawczyn G, Zurita R, Pereira J. The Use of AI by First-Year Medical Students: A Qualitative Study of Perspectives, Usage, and Recommendations. MedEdPublish 2025;15:226 View
  14. Simoni J, Urtubia-Fernandez J, Mengual E, Simoni D, Royo M, Egaña-Yin D, Hertog O, López-Ortiz L, Muñoz-Tomás A, Santiago-Martínez P, Vahamaki A, Pereira J. Artificial intelligence in undergraduate medical education: an updated scoping review. BMC Medical Education 2025;25(1) View

Books/Policy Documents

  1. Rosmayanti V. AI Use in Social Sciences. View
  2. Singh H, Kumaran S, Dhamija E. Imaging in Management of Breast Diseases. View
  3. Çela E, Paliktzoglou V, Vajjhala N. Digital Transformation in Interdisciplinary Sciences. View