Undergraduate Medical Students’ and Interns’ Knowledge and Perception of Artificial Intelligence in Medicine
Bibliographic record
Abstract
Nisha Jha,1 Pathiyil Ravi Shankar,2 Mohammed Azmi Al-Betar,3 Rupesh Mukhia,4 Kabita Hada,5 Subish Palaian6,7 1Department of Clinical Pharmacology and Therapeutics, KIST Medical College and Teaching Hospital, Lalitpur, Bagmati, Nepal; 2IMU Centre for Education, International Medical University, Kuala Lumpur, Kuala Lumpur Federal Territory, Malaysia; 3Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates; 4Department of Surgery, KIST Medical College, Lalitpur, Bagmati, Nepal; 5Department of General Practice and Emergency Medicine, KIST Medical College, Lalitpur, Bagmati, Nepal; 6Department of Clinical Sciences, College of Pharmacy and Health Sciences Ajman University, Ajman, United Arab Emirates; 7Center of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab EmiratesCorrespondence: Nisha Jha, Department of Clinical Pharmacology and Therapeutics, KIST Medical College and Teaching Hospital, Lalitpur, Bagmati, Nepal, Tel +977 9841602808, Email nishajha32@gmail.com Subish Palaian, Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates, Tel +971 551322957, Email subishpalaian@gmail.comPurpose: 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.Keywords: artificial intelligence, healthcare, machine learning, medical students, Nepal
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".