Knowledge, Attitude, And Practice of Artificial Intelligence Among Undergraduate Medical Students: A Cross-Sectional Study
DOI:
https://doi.org/10.52783/jns.v14.2327Keywords:
Artificial Intelligence, Knowledge, Attitude, PracticeAbstract
Background: Artificial Intelligence (AI) is revolutionizing the medical field, with applications in medical imaging, patient management, and predictive analytics. With the increasing presence of AI in medicine, medical students need to be imparted with knowledge, a good attitude, and proper practices in order to incorporate AI into clinical decision-making.
Objectives: The purpose of this study is to assess the knowledge, attitude, and practice (KAP) of undergraduate medical students towards AI, determining gaps and areas of improvement in medical education.
Methods: A cross-sectional study was carried out among 250 undergraduate medical students. A structured questionnaire with 21 items (7 each for knowledge, attitude, and practice) was administered, with responses noted on a five-point Likert scale. Descriptive statistics were used to analyze the data.
Results: A good 65% of the participants had excellent familiarity with AI, particularly in medicine imaging and diagnostics. Just 40% understood AI usage in drug discovery and genomics, though. A 72% majority was certain that AI would enhance healthcare and that 68% wanted training in AI. Still, a 55% majority was anxious that AI would decrease clinical thinking. In terms of practice, only 35% had previously used AI-based tools for medical learning, and 48% were unsure how to apply AI in clinical settings.
Conclusion: While students show a positive attitude toward AI, gaps exist in knowledge and practical implementation. Integrating AI education into the medical curriculum is necessary to enhance competency. Further research should assess the long-term impact of AI exposure on medical training.
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Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9.
Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225. 2017.
Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719-31.
Paranjape K, Schinkel M, Panday RN, Car J, Nanayakkara P. The effect of artificial intelligence on the future of medicine and medical education. Front Med. 2019; 6:39.
Sarwar S, Dent A, Faust K, Richer M, Djuric U, Van Ommeren R, et al. Physician perspectives on integration of artificial intelligence into diagnostic pathology. NPJ Digit Med. 2019;2(1):28.
Wang F, Kaushal R, Khullar D. Should healthcare embrace or resist artificial intelligence? NPJ Digit Med. 2019; 2:19.
Kolachalama VB, Garg PS. Machine learning and medical education. NPJ Digit Med. 2018;1:54.
Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018;68(668):143-4.
Tran BX, Vu GT, Ha GH, Vuong QH, Ho MT, Vuong TT, et al. Global evolution of research in artificial intelligence in health and medicine: A bibliometric study. J Clin Med. 2019;8(3):360.
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43.
Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med Educ. 2019;5(2):e16048.
Alexander AG, Ball CA, Lukowicz P. Deep learning in medical education: A new paradigm. Med Educ Online. 2020;25(1):1750192.
Wang CJ, Berman RS, Bello JA, Martin JF. The role of artificial intelligence in medical education. Med Educ. 2019;53(12):1229-36.
Kolachalama VB, Garg PS. Machine learning education for future physicians. NPJ Digit Med. 2018; 1:54.
Wartman SA, Combs CD. Medical education must move from the information age to the age of artificial intelligence. Acad Med. 2018;93(8):1107-9.
Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: Integrative review. JMIR Med Educ. 2019;5(1):e13930.
Patel VL, Shortliffe EH, Stefanelli M, Szolovits P, Berthold MR, Bellazzi R, et al. The coming of age of artificial intelligence in medicine. Artif Intell Med. 2009;46(1):5-1
Yang J, Yeung S, Park J, Shuang F, Kahn CE Jr, Rubin DL. Machine learning for medical education: An introduction. Acad Radiol. 2021;28(1):107-19.
Meskó B, Hetényi G, Győrffy Z. Will artificial intelligence solve the human resource crisis in healthcare? BMC Health Serv Res. 2018;18(1):545.
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