Artificial intelligence in odontology education
Related Experiment Video
Updated: Nov 12, 2026
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
María Belén Morales-Cevallos1, Canva Byron Ma Lam2, Kathy Violeta Serrano Avalos3
1Universidad Ecotec, Samborondón, Ecuador.
Introduction:
Artificial intelligence (AI) is increasingly being explored in dental education as a tool to support teaching, learning, assessment, and clinical decision-making. This systematic review synthesized current evidence on the implementation and educational impact of AI technologies in undergraduate, postgraduate, and continuing dental education.
Methodology:
A PRISMA-guided systematic review was conducted using Scopus, PubMed, and IEEE Xplore. Studies published between 2020 and 2025 evaluating AI applications in dental education were identified using predefined PICO criteria. Educational outcomes included knowledge acquisition, diagnostic accuracy, clinical reasoning, procedural skills, learner engagement, and perceptions of AI-assisted learning.
Results:
The included literature reflects an expansion from machine learning, deep learning, and computer vision systems primarily evaluated for diagnostic support toward generative AI platforms, including large language models and retrieval-augmented generation systems. Across the included studies, AI applications were frequently associated with favorable diagnostic and examination performance and, in selected educational contexts, with supportive findings related to clinical reasoning, technical competency, feedback, and self-directed learning. However, the evidence was predominantly based on technical performance assessments and observational or cross-sectional designs.
Discussion:
AI is increasingly being explored as a potential complementary educational resource, with applications including interactive tutoring, adaptive learning, and support for clinical reasoning. Nevertheless, heterogeneous methodologies and limited longitudinal evidence indicate the need for standardized educational evaluation and responsible curricular integration.
Conclusion:
AI shows potential as a complementary resource in dental education. Its integration should remain pedagogically guided and ethically supervised while future research establishes its educational effectiveness through standardized and longitudinal evaluation.