Artificial intelligence in medical education - perception among medical students
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Abstract
Background: As Artificial Intelligence (AI) becomes pervasive in healthcare, including applications like robotic surgery and image analysis, the World Medical Association emphasises integrating AI education into medical curricula. This study evaluates medical students’ perceptions of ‘AI in medicine’, their preferences for AI training in education, and their grasp of AI’s ethical implications in healthcare. Materials & methods: A cross-sectional study was conducted among 325 medical students in Kerala using a pre-validated, semi structured questionnaire. The survey collected demographic data, any past educational experience about AI, participants’ self-evaluation of their knowledge and evaluated self-perceived understanding of applications of AI in medicine. Participants responded to twelve Likert-scale questions targeting perceptions and ethical aspects and their opinions on suggested topics on AI to be included in their curriculum. Results & discussion: AI was viewed as an assistive technology for reducing medical errors by 57.2% students and 54.2% believed AI could enhance medical decision accuracy. About 49% agreed that AI could potentially improve accessibility to healthcare. Concerns about AI replacing physicians were reported by 37.6% and 69.2% feared a reduction in the humanistic aspect of medicine. Students were worried about challenges to trust (52.9%), patient-physician relationships (54.5%) and breach of professional confidentiality (53.5%). Only 3.7% felttotally competent in informing patients about features and risks associated with AI applications. Strong demand for structured AI training was expressed, particularly on reducing medical errors (76.9%) and ethical issues (79.4%). Conclusion: This study highlights medical students’ demand for structured AI training in undergraduate curricula, emphasising its importance in addressing evolving healthcare needs and ethical considerations. Despite widespread ethical concerns, the majority perceive AI as an assistive technology in healthcare. These findings provide valuable insights for curriculum development and defining learning outcomes in AI education for medical students.
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243 out of 325 respondents (74.8%) highlighted structured AI training's role in reducing medical errors.
“Structured AI training was highly valued, with 243 out of 325 respondents (74.8%) highlighting its role in reducing medical errors”
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 [46]
The Results section, “Need for training on AI in medical curriculum” (page unavailable), reports that 243 of 325 respondents (74.8%) favoured structured AI training in general. It does not assign that figure to reducing medical errors; the Abstract’s “Results & discussion” instead reports 76.9% for demand for training on that topic. (LLM, conf=0.90)
Experimental — accuracy not yet measuredAI's perceived potential to reduce diagnostic errors was a driver of enthusiasm, with 57.2% in India.
“AI’s perceived potential to reduce diagnostic errors (57.2% in India) [46]”
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 [46]
The Abstract’s Results & discussion reports that 57.2% of surveyed students viewed AI as helping to reduce medical errors. The Results and Discussion link perceived error reduction to favourable views of AI, but do not identify the 57.2% figure as specifically about diagnostic errors; the Results instead report 72.3% agreement that AI ‘reduces error in medical practice.’ The study surveyed students at one institution in Kerala, India. (LLM, conf=0.87)
Experimental — accuracy not yet measured258 out of 325 respondents (79.4%) emphasized structured AI training's relevance to ethical decision-making.
“258 out of 325 respondents (79.4%) emphasizing its relevance to ethical decision-making [46].”
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- Authors
- Jackson, P., Ponath Sukumaran, G., Babu, C., Tony, M. C., Jack, D. S., Reshma, V. R., Davis, D., Kurian, N., & John, A.
- Affiliations
- Pushpagiri Medical College (IN)
- Venue
- BMC Medical Education
- Published
- 2024-07-27
- Volume
- 24
- Issue
- 1
- Pages
- 804-804
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC11283685
- OpenAlex
- W4401046213
- Cited by
- 185
- References
- 35
- Citations by year
- 2026: 110 · 2025: 63 · 2024: 12
- FWCI
- 17.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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' perceptions, ethics of AI in healthcare, medical errors, physician-patient trust, professional secrecy, AI training needs, undergraduate medical education
- MeSH terms
- Adult, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Curriculum, Education, Medical, Education, Medical, Undergraduate, Female, Humans, India, Male, Surveys and Questionnaires, Students, Medical, Young Adult
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