Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.

Artificial Intelligence in Dental Education: Overview of Teaching, Assessment and Academic Performance Prediction

Artificial Intelligence (AI) is increasingly used in dental education. The present study aimed to examine the current landscape of AI applications in dental education, focusing on their impact on teaching, assessment, and the prediction of academic performance. A literature search was performed based on articles publis...

Artificial intelligence for radiographic caries detection in dental education: a systematic review with functional meta-synthesis.

Machine Learning and Artificial Intelligence Models Applied to the Detection and Classification of Dental Caries: A Systematic Literature Review

AI/ML systems demonstrate substantial diagnostic potential for dental caries, frequently matching clinician performance, and critical gaps persist regarding multicenter validation, clinically interpretable models, and prospective trials evaluating patient outcomes.

Artificial intelligence and technology in paediatric dentistry: a review

The field of pediatric dentistry is undergoing a paradigm shift, moving from a reactive model to one that embraces precision, prevention and the power of Artificial Intelligence (AI) and Digital Technologies (DT). These innovations offer exciting opportunities to transform diagnosis, management and treatment of paediat...

Teaching Methodologies and Educational Outcomes of Point‐of‐Care Ultrasound in Undergraduate Medical Education: A Scoping Review

The structured and longitudinal integration of POCUS into undergraduate medical curricula appears to be a promising strategy for strengthening clinical reasoning, diagnostic accuracy and bedside decision‐making and the importance of longitudinal curricular integration for sustainable competency development is highlight...

AI-driven Dentistry

Artificial intelligence has the potential to improve diagnostic consistency, treatment planning, risk prediction, and personalized dental care and should augment rather than replace clinicians’ professional judgment.