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

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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.

Related collections

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Author and article information

Contributors

Julia-Astrid Moldt:

ORCID: http://orcid.org/0000-0002-2418-150X

Teresa Festl-Wietek:

ORCID: http://orcid.org/0000-0003-1450-1757

Wolfgang Fuhl:

ORCID: http://orcid.org/0000-0001-7128-298X

Susanne Zabel:

ORCID: http://orcid.org/0000-0003-3374-149X

Manfred Claassen:

ORCID: http://orcid.org/0000-0002-4583-9083

Samuel Wagner:

ORCID: http://orcid.org/0000-0003-1808-3556

Kay Nieselt:

ORCID: http://orcid.org/0000-0002-1283-7065

Anne Herrmann-Werner:

ORCID: http://orcid.org/0000-0003-2413-7047

Journal

Journal ID (nlm-ta): JMIR Med Educ

Journal ID (iso-abbrev): JMIR Med Educ

Journal ID (hwp): JME

Journal ID (publisher-id): mededu

Journal ID (index): 20

Title: JMIR Medical Education

Publisher: JMIR Medical Education

ISSN (Electronic): 2369-3762

Publication date Collection: 2024

Publication date (Electronic): 12 June 2024

Volume: 10

Electronic Location Identifier: e58355

Affiliations

[1 ]departmentTübingen Institute for Medical Education , University of Tübingen , Tübingen, Germany

[2 ]departmentInstitute for Bioinformatics and Medical Informatics , University of Tübingen , Tübingen, Germany

[3 ]departmentDepartment of Computer Science , University of Tübingen , Tübingen, Germany

[4 ]departmentDepartment of Internal Medicine , University Hospital of Tübingen , Tübingen, Germany

[5 ]departmentBoard of the Faculty of Medicine , University of Tübingen , Tübingen, Germany

[6 ]departmentDepartment of Internal Medicine VI - Psychosomatic Medicine and Psychotherapy , University of Tübingen , Tübingen, Germany

Author notes

Julia-AstridMoldtMA, Tübingen Institute for Medical Education, University of Tübingen, Elfriede-Aulhorn-Str 10, Tübingen, 72076, Germany, 49 7071 29-73720; julia-astrid.moldt@ 123456med.uni-tuebingen.de

None declared.

Author information

Julia-Astrid Moldt http://orcid.org/0000-0002-2418-150X

Teresa Festl-Wietek http://orcid.org/0000-0003-1450-1757

Wolfgang Fuhl http://orcid.org/0000-0001-7128-298X

Susanne Zabel http://orcid.org/0000-0003-3374-149X

Manfred Claassen http://orcid.org/0000-0002-4583-9083

Samuel Wagner http://orcid.org/0000-0003-1808-3556

Kay Nieselt http://orcid.org/0000-0002-1283-7065

Anne Herrmann-Werner http://orcid.org/0000-0003-2413-7047

Article

Publisher ID: 58355

DOI: 10.2196/58355

PMC ID: 11238140

PubMed ID: 38989834

SO-VID: 9e669564-2776-43c6-8584-44362fba1964

Copyright © Copyright © Julia-Astrid Moldt, Teresa Festl-Wietek, Wolfgang Fuhl, Susanne Zabel, Manfred Claassen, Samuel Wagner, Kay Nieselt, Anne Herrmann-Werner. Originally published in JMIR Medical Education (https://mededu.jmir.org)

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on https://mededu.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 13 March 2024

Date revision received : 16 April 2024

Date accepted : 07 May 2024

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