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Related Experiment Videos

Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.

Mohammed Turky1, Lakshman Samaranayake2, Thanaphum Osathanon3

  • 1Department of Endodontics, Faculty of Dentistry, Minia University, Minia, Egypt; Department of Endodontics, Faculty of Dentistry, Sphinx University, Assiut, Egypt.

International Dental Journal
|June 22, 2026
PubMed
Summary

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This summary is machine-generated.

Federated learning (FL) enables collaborative AI development in endodontics without sharing patient data. This approach enhances AI model accuracy and generalisability while prioritizing patient privacy.

Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Informatics
  • Machine Learning in Endodontics

Background:

  • High-quality AI models in endodontics require diverse, well-annotated datasets.
  • Centralized AI training faces legal and ethical challenges due to data protection regulations.
  • Federated learning (FL) offers a privacy-preserving framework for collaborative AI development.

Purpose of the Study:

  • Introduce federated learning (FL) as a solution for collaborative AI in endodontics.
  • Explore the principles, applications, and challenges of FL in endodontic AI.
  • Outline implementation pathways and research priorities for FL in endodontics.

Main Methods:

  • Narrative review of literature up to April 2026.
  • Comprehensive search strategy across multiple databases.
Keywords:
Artificial intelligenceData privacyEndodonticsFederated learning

Related Experiment Videos

  • Comparative analysis of FL fundamentals and applications for diagnostic and decision-support tasks.

    • Main Results:

    • FL allows institutions to retain local patient data while contributing model updates.
    • Exploration of FL principles, privacy mechanisms, architectures, and challenges.
    • Proposed roadmap for FL implementation: pilot studies, data standardization, clinical validation, and regulatory engagement.
    • FL enhances AI development in endodontics, safeguarding patient privacy for improved diagnostics and personalized care.

    Conclusions:

    • Federated learning can advance AI integration in endodontics, prioritizing patient privacy.
    • FL enables multicenter AI model development without data sharing, improving accuracy and generalizability.
    • This approach holds potential for enhanced endodontic diagnosis, treatment planning, and outcome prediction.