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Artificial Intelligence and ChatGPT in Medical Education: A Cross-Sectional Questionnaire on students' Competence
L Maaß1, C Grab-Kroll2, J Koerner2
1Institute for Anatomy and Cell Biology, Faculty of Medicine, Ulm University, Ulm, Germany.
Journal of CME
|January 8, 2025
Summary
Medical students show high familiarity with ChatGPT but lack confidence in advanced AI applications. They desire targeted training in artificial intelligence for their professional roles, highlighting a need for improved AI literacy in health science education.
Area of Science:
- Health Science Education
- Medical Informatics
- Artificial Intelligence in Medicine
Background:
- Artificial intelligence (AI) is increasingly impacting health sciences and medical education.
- Student competencies regarding AI knowledge, skills, and attitudes remain underexplored.
- Understanding AI tool application, like ChatGPT, is crucial for future healthcare professionals.
Purpose of the Study:
- To assess medical students' competencies in applying AI tools, focusing on knowledge, skills, and attitudes.
- To identify current usage patterns and familiarity with AI technologies among medical students.
- To gauge student interest in AI-related training tailored to their professional development.
Main Methods:
- A unicentric, questionnaire-based, cross-sectional study was conducted.
- The study involved 207 students from the medical field.
- Data collection focused on AI tool familiarity, usage, perceived knowledge gaps, and training needs.
Main Results:
- High familiarity (66.7%) with ChatGPT was reported, but other AI tools were less known/used.
- Students often used AI informally, like search engines, and felt uninformed about AI technology.
- Significant uncertainty exists regarding prompt engineering and legal implications of AI use.
- Most students expressed interest in AI but lacked confidence and desired role-specific training.
- Preferred AI learning topics included AI in studies (62.5%), AI in general (58.0%), and AI in scientific writing (57.0%).
Conclusions:
- Medical students are receptive to AI but require structured education to bridge knowledge and skill gaps.
- Training should focus on practical AI applications relevant to medical practice and scientific inquiry.
- Addressing AI's technological and legal aspects is essential for confident and ethical student adoption.
- Future research should consider demographic factors like gender and academic performance in AI competency assessments.
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