Artificial intelligence in periodontics and peri-implant medicine: from diagnosis to precision care
Authors:
Open Access
- 4 May 2026
- journal article
- review article
- Published by Frontiers Media SA in Frontiers in Dental Medicine
- Vol. 7, 1816844
Abstract
Background: Artificial intelligence (AI) has substantial potential to advance periodontal and peri- implant care through novel diagnostic, prognostic, and therapeutic opportunities. Nevertheless, the evidence is scattered over several fields. Objective: To provide a narrative synthesis of the recent evidence regarding applications of AI to periodontology and peri-implant medicine specifically diagnostic imaging, disease prediction, biomarker discovery, peri-implant disease assessment and clinical decision support. Methods: A systematic search of PubMed, MEDLINE, Scopus, and Embase for studies from August 2020 to August 2025 was performed. We included only original research articles on AI applications in periodontal or peri-implant settings. After screening the identified records and conducting full-text assessments, a total of 32 studies were included based on established inclusion criteria. Results: AI showed high diagnostic performance at imaging-based applications, with accuracy from 0.85 to 0.98 reported for the detection and quantification of alveolar bone loss using two- and three-dimensional radiographs. Using this data, machine learning (ML) models were developed for identifying potential molecular biomarkers that could support disease prediction and gain insight into biological processes. AI-aided immune profiling and risk stratification models demonstrated robust predictive capabilities in peri-implant maintenance. mHealth is one of the types of AI-based digital health devices and has succeeded in enhancing patient compliance and periodontal prognosis. Conclusions: AI has considerable promise to impact non-invasive periodontal and peri-implant diagnostics, risk-assessment and enhanced treatment-planning through advanced imaging analysis, molecular profiling as well as patient-centred digital tools. These need to be further validated, standardized, and integrated into clinical care before widespread uptake.Keywords
This publication has 38 references indexed in Scilit:
- Interpretable Ensemble Learning Predicts Antibiotic Resistance in Treponema denticola Using Expert ClassifiersInternational Dental Journal, 2025
- Neural Networks for Predicting and Classifying Antimicrobial Resistance Sequences in Porphyromonas gingivalisInternational Dental Journal, 2025
- Leveraging autoencoder models and data augmentation to uncover transcriptomic diversity of gingival keratinocytes in single cell analysisScientific Reports, 2025
- An explainable RoBERTa approach to analyzing panic and anxiety sentiment in oral health education YouTube commentsScientific Reports, 2025
- Identification of potential shared gene signatures between periodontitis and breast cancer by integrating bulk RNA-seq and scRNA-seq dataScientific Reports, 2025
- Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomographyDentomaxillofacial Radiology, 2025
- Performance of ChatGPT in classifying periodontitis according to the 2018 classification of periodontal diseasesClinical Oral Investigations, 2024
- Automated periodontitis bone loss diagnosis in panoramic radiographs using a bespoke two-stage detectorComputers in Biology and Medicine, 2022
- Classification of periodontitis stages in mandibular area from dental panoramic radiograph using Adaptive Center Line-Distance Based image processing approachJournal of Ambient Intelligence and Humanized Computing, 2022
- How frequent does peri-implantitis occur? A systematic review and meta-analysisClinical Oral Investigations, 2017