ReviewInternational dental journal2026
Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.
Review in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION AND
aimHigh-quality artificial intelligence (AI) models in endodontics require access to diverse, well-annotated datasets. This review introduces federated learning (FL) as a privacy-preserving framework for collaborative AI in endodontics.
methodsIn this narrative review, a comprehensive literature search was conducted across databases, encompassing studies published up to April 2026. The search strategy was intentionally broad to facilitate a thorough exploration of the evolution of the relevant concepts. However, to ensure consistency in comprehension and analysis, the review was limited to publications in English. Studies describing the fundamentals and applications of FL were reviewed and comparatively analysed. The narrative review served as the framework for outlining implementation pathways, challenges, and research priorities for its application to diagnostic and decision-support tasks.
resultsTraditional centralised training methods face legal and ethical challenges due to data protection regulations. FL allows institutions to retain local patient data while contributing model updates to a central server or decentralised network, thus providing a viable alternative. The article explores FL principles, privacy and security mechanisms, architectures, technical challenges, and adversarial risks. Regulatory and ethical considerations hinge on a mix of advanced technical measures and robust organisational practices. A proposed roadmap for implementing FL includes pilot studies, standardised data processes, clinical validation, and regulatory engagement. FL promises to enhance AI development in endodontics while safeguarding patient privacy, with potential benefits in diagnostics and personalised care.
conclusionFL could advance AI integration in endodontics, prioritising the protection of patient privacy. This initiative holds the potential to improve diagnostic processes and facilitate personalised treatment approaches. CLINICAL RELEVANCE: FL enables the development of multicentre AI models without sharing patient data. By leveraging diverse clinical datasets, this approach may improve the accuracy and generalisability of AI systems for endodontic diagnosis, treatment planning, and outcome prediction while preserving patient privacy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.