Do Medical Students Still Need Practicum and Laboratory Classes in the Age of Artificial Intelligence?

Siagian, Forman Erwin (2024) Do Medical Students Still Need Practicum and Laboratory Classes in the Age of Artificial Intelligence? Asian Journal of Advanced Research and Reports, 18 (11). pp. 149-160. ISSN 2582-3248

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Abstract

Aims: to revisited the irreplaceability of practicum and laboratory classes for medical student and its benefit, even in the artificial intelligence (AI) era. Discussion: The transformative potential of AI in every aspect of human also reshape the pedagogy of medical education. Integrating AI into medical education offers numerous potential benefits, including improved curriculum design and evaluation and the ability to refine the delivery of objective student assessment, better clinical simulation organization, and enhanced education transparency. On contrary, the main challenges of AI integration in medical education are ethical and legal issues, scalability limitations, technical difficulties, and evaluating the effectiveness of traditional educational methods such as practicum or laboratory classes. Fortunately, Practicums is still considering important because they provide students with real-world hands on experience and the opportunity to apply whatever theory taught in the classroom to practice. Furthermore, it can integrate knowledge and better understanding which acquired through coursework and other combined learning experiences. AI does not have the capability to replace human contact and moral values in practicum. The balance between AI and traditional hands-on learning is a must and the benefits of AI incorporation to medical curriculum, including practicum and laboratory classes with the enduring need for practical experiences will benefit all stakeholder. Conclusion: The irreplaceability of hands-on training and the balance between AI and human contact in medical education make traditional practicum and laboratory classes are still relevant, even in the era of AI. Keywords: Knowledge engineering; machine learning; medical education; skill; hands on experience.

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