Undergraduate Medical Students’ and Interns’ Knowledge and Perception of Artificial Intelligence in Medicine
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».