ReviewFrontiers in dental medicine2026
The impact of artificial intelligence on periodontal disease detection and treatment.
Review in Frontiers in dental medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Periodontal disease (PD) is one of the most prevalent chronic inflammatory non-comunicable diseases worldwide. Early diagnosis and timely intervention for periodontitis are essential to prevent the onset and progression of the disease, especially due to the associated risk, sometimes subclinical, of negative correlations with various systemic diseases that impair quality of life. In this regard, artificial intelligence (AI) has emerged as a transformative tool in healthcare, and its application in the detection and treatment of periodontal disease holds considerable promise. This study aims to review and update the latest evidence on the role of AI in the diagnosis and management of periodontal disease, emphasising advancements in machine learning (ML) algorithms, diagnostic imaging, and predictive modelling. Moreover, it is analyzed how AI-driven technologies, such as deep learning models applied to radiographs and clinical data, can enhance diagnostic accuracy, predict disease progression, and assist in personalized treatment planning. The potential of AI to optimise clinical workflows and improve patient outcomes is also discussed, alongside the challenges of integrating it into routine dental practice, including ethical considerations and data privacy concerns. This review highlights the current state of AI in periodontology, identifies key research gaps, and offers recommendations for future directions in AI-driven periodontal care.
Indexed as
Identifiers
What OpenQuestion holds
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.