Undergraduate Medical Students’ and Interns’ Knowledge and Perception of Artificial Intelligence in Medicine
Open accessAbstract
Purpose: Artificial intelligence (AI) is playing an increasingly important role in healthcare and health professions education. This study explored medical students' and interns' knowledge of artificial intelligence (AI), perceptions of the role of AI in medicine, and preferences around the teaching of AI competencies. Methods: In this cross-sectional study, the authors used a previously validated Canadian questionnaire and gathered responses from students and interns at KIST Medical College, Nepal. Face validity and reliability of the tool were assessed by administering the questionnaire to 20 alumni as a pilot sample (Cronbach alpha = 0.6). Survey results were analyzed quantitatively (p-value = 0.05). Results: In total 216 students (37% response rate) participated. The median AI knowledge score was 11 (interquartile range 4), and the maximum possible score was 25. The score was higher among final year students (p = 0.006) and among those with additional training in AI (p = 0.040). Over 49% strongly agreed or agreed that AI will reduce the number of jobs for doctors. Many expect AI to impact their specialty choice, felt the Nepalese health-care system is ill-equipped to deal with the challenges of AI, and opined every student of medicine should receive training on AI competencies. Conclusion: The lack of coverage of AI and machine learning in Nepalese medical schools has resulted in students being unaware of AI's impact on individual patients and the healthcare system. A high perceived willingness among respondents to learn about AI is a positive sign and a strong indicator of futuristic successful curricula changes. Systematic implementation of AI in the Nepalese healthcare system can be a potential tool in addressing health-care challenges related to resource and manpower constraints. Incorporating topics related to AI and machine learning in medical curricula can be a useful first step.
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In Jha et al., 76.2% of participants believed that every medical student or trainee should receive training on AI competencies.
“In Jha et al., 76.2% of participants believed that every medical student or trainee should receive training on AI competencies [47].”
From Is it time to introduce an artificial intelligence curriculum in undergraduate medical education? medical students’ perspectives: a systematic review and meta-analysis (2026) · cites this paper as [47]
The Results section says that over 80% of respondents believed every medical student or trainee should receive training on AI competencies. It supports the reported belief, but not the attributed figure of 76.2%; the Discussion describes the proportion as over 85%. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThis review incorporates evidence from LMICs, illuminating context-specific challenges such as the post-war healthcare environment in Syria and curriculum constraints in Nepal.
“this review incorporates evidence from LMICs, thereby illuminating context-specific challenges such as the post-war healthcare environment in Syria [42] and curriculum constraints in Nepal [47].”
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- Authors
- Jha, N., Shankar, P. R., Al-Betar, M. A., Mukhia, R., Hada, K., & Palaian, S.
- Affiliations
- KIST Medical College (NP)
- IMU University (MY)
- Ajman University (AE)
- Corresponding author
- Jha, Nisha
- Palaian, Subish
- Venue
- Advances in Medical Education and Practice
- Publisher
- Informa UK Limited
- Published
- 2022-08
- Volume
- Volume 13
- Pages
- 927-937
- ISSN
- 1179-7258 (ISSN-L)
- PMC
- PMC9419901
- OpenAlex
- W4293282468
- Cited by
- 143
- References
- 31
- Citations by year
- 2026: 36 · 2025: 49 · 2024: 39 · 2023: 18 · 2022: 1
- FWCI
- 8.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advances in Medical Education and Practice (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- University of Toronto
- 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 students, medical interns, AI knowledge, perceptions of AI, AI competencies, machine learning, job displacement fears, specialty choice
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