• Artificial Intelligence in Med...

*Result*: Artificial Intelligence in Medical Education: Transformative Potential, Current Applications, and Future Implications.

Title:
Artificial Intelligence in Medical Education: Transformative Potential, Current Applications, and Future Implications.
Authors:
Izquierdo-Condoy JS; One Health Research Group, Universidad de Las Américas, Via Nayon S/N, Quito, 170124, Ecuador, +593 995760693., Arias-Intriago M; One Health Research Group, Universidad de Las Américas, Via Nayon S/N, Quito, 170124, Ecuador, +593 995760693., Montero Corrales L; Escuela de Comunicación, Universidad Latina de Costa Rica, San Jose, Costa Rica., Ortiz-Prado E; One Health Research Group, Universidad de Las Américas, Via Nayon S/N, Quito, 170124, Ecuador, +593 995760693.
Source:
JMIR medical education [JMIR Med Educ] 2026 Feb 17; Vol. 12, pp. e77127. Date of Electronic Publication: 2026 Feb 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101684518 Publication Model: Electronic Cited Medium: Internet ISSN: 2369-3762 (Electronic) Linking ISSN: 23693762 NLM ISO Abbreviation: JMIR Med Educ Subsets: MEDLINE
Imprint Name(s):
Original Publication: Toronto, ON : JMIR Publications, [2015]-
MeSH Terms:
Artificial Intelligence*/trends , Education, Medical*/methods , Education, Medical*/trends, Humans ; Curriculum ; Forecasting ; Machine Learning ; Natural Language Processing
Contributed Indexing:
Keywords: adaptive learning systems; artificial intelligence; future implications; machine learning; medical education
Entry Date(s):
Date Created: 20260217 Date Completed: 20260217 Latest Revision: 20260219
Update Code:
20260219
PubMed Central ID:
PMC12912660
DOI:
10.2196/77127
PMID:
41701936
Database:
MEDLINE

*Further Information*

*Unlabelled: Artificial intelligence (AI) is increasingly influencing medical education by enabling adaptive learning, AI-assisted assessment, and scalable instructional tools. Natural language processing, machine learning, and generative large language models offer innovative ways to support teaching and learning, yet their integration raises ethical, pedagogical, and infrastructural challenges. This viewpoint article aims to examine the current applications, benefits, and challenges of AI in medical education and propose strategies for responsible and effective integration. AI tools such as chatbots, virtual patients, and intelligent tutoring systems enhance personalized and immersive learning. Automated grading and predictive analytics support efficient evaluations, while AI-assisted writing tools streamline content creation. Despite these advances, concerns persist around data privacy, algorithmic bias, unequal access, and diminished critical thinking. Key solutions include AI literacy training, data oversight, equitable infrastructure, and curriculum reform. The FACETS framework offers 6 dimensions (ie, form, application, context, instructional mode, technology, and the SAMR [substitution, augmentation, modification, redefinition model]) to evaluate AI integration effectively. AI offers substantial opportunities to transform medical education, but its adoption must be ethical, equitable, and pedagogically grounded. Strategic frameworks such as FACETS, combined with institutional governance and cross-sector collaboration, are essential to guide implementation so that AI enhances learning outcomes while preserving the humanistic foundations of medical practice.
(© Juan S Izquierdo-Condoy, Marlon Arias-Intriago, Laura Montero Corrales, Esteban Ortiz-Prado. Originally published in JMIR Medical Education (https://mededu.jmir.org).)*