Undergraduate Medical Students’ and Interns’ Knowledge and Perception of Artificial Intelligence in Medicine

• Published in: Advances in medical education and practice Vol. 13; pp. 927 - 937
• Main Authors: Jha, Nisha, Shankar, Pathiyil Ravi, Betar, Mohammed Azmi Al, Mukhia, Rupesh, Hada, Kabita, Palaian, Subish
• Format: Journal Article
• Language: English
• Published: Macclesfield Dove Medical Press Limited 01-01-2022; Taylor & Francis Ltd; Dove; Dove Medical Press
• Subjects: Alzheimer's disease; Artificial intelligence; College students; Consent; Curricula; Ethics; healthcare; Learning; Learning strategies; Machine learning; Medical colleges; Medical education; Medical schools; Medical students; nepal; Original Research; Perceptions; Physicians; Precision medicine; Questionnaires; Regulatory approval; Social networks; Student attitudes; Teaching hospitals; Canada; Nepal; United Arab Emirates
• ISSN: 1179-7258; 1179-7258
• DOI: 10.2147/AMEP.S368519

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. Keywords: artificial intelligence, healthcare, machine learning, medical students, Nepal