Skip to content
Home▶ Journals▶ Academia Journal of Medicine▶Archive▶Vol. 9 No. 2 (2026)▶Artificial Intelligence in Periodontics: A Narrative Review…
Academia Journal of Medicine
Academia Journal of Medicine ›
Print ISSN 2320-8643 · Online ISSN 2320-8651
Submit your paper About the journal
Review Article

Artificial Intelligence in Periodontics: A Narrative Review of Clinical Applications, Evidence, and Challenges

, , ,
Sneh Agrawal — Associate professor, Dept of Periodontology, Bharati Vidyapeeth (deemed to be) University Dental College and Hospital, Navi Mumbai, Maharashtra.
Reshma Avhad — Reader, Associate Professor, Dept of Periodontology, Dr Rajesh Ramdasji Kambe Dental College and Hospital, Akola, Maharashtra.
Mrudula Sabale — 3rd year MDS student, Dept of Periodontology, Yogita Dental College and Hospital, Khed, Maharashtra.
Rashmi Laddha — Professor and HOD, Dept of Periodontology, Dr Rajesh Ramdasji Kambe Dental College and Hospital, Akola, Maharashtra.
Published 31 Oct 2026 Vol. 9 No. 2 (2026), pp. 206-213 ● Open access
❝ How to cite 🔗 https://doi.org/10.48165/ajm.2026.9.02.35
Formats:Full text (HTML)JATS XMLPDF ● Open access
Download PDF

Abstract

Background: Periodontitis is a chronic inflammatory disease that damages the tissues and bone supporting teeth and is linked to systemic conditions1. Its diagnosis, staging, and long-term management depend on the integration of clinical, radiographic, and risk-factor data under the 2017 World Workshop staging-and-grading framework2 — tasks that are time-consuming, examiner-dependent, and well suited to computational support. Artificial intelligence (AI), particularly deep learning for image analysis and machine learning for prediction, has attracted rapidly growing interest in periodontology3.

Purpose: To narratively synthesize the current evidence on AI applications across the periodontal care pathway, including radiographic detection of bone loss, diagnostic and staging support, risk and prognosis prediction, treatment-response forecasting, supportive care, peri-implant disease assessment, and generative AI tools.

Findings: The most mature application is radiographic analysis. Convolutional neural networks detect and quantify alveolar bone loss on panoramic and periapical radiographs with accuracy comparable to or exceeding experienced dentists in controlled settings4. Pooled estimates from a systematic review indicate sensitivity of approximately 87% for detecting periodontal bone loss, though specificity is weaker — 69% in the primary meta-analysis, rising to 76% only after excluding one outlier study1. Automated methods can stage periodontitis from radiographs, but radiographic output supplies only part of the information required by the 2017 World Workshop staging-and-grading framework2. Machine learning models predict tooth loss and one-year treatment response with moderate-to-good discrimination; external validation remains uncommon, and performance can vary across settings5. For peri-implant disease, deep learning models detect radiographic marginal bone loss with high pooled accuracy, but the certainty of evidence has been rated very low and none of the eight studies in the 2026 Odontology review used external validation6. An AI-enabled toothbrush with remote coaching improved 6-month outcomes as an adjunct to therapy in a randomized trial7, and large language models answer periodontal questions at a "good but not excellent" level with readability exceeding that recommended for patient-facing material8.

Conclusions: AI is a promising adjunct — not a replacement — for the periodontist. Technical performance in retrospective datasets has outpaced demonstration of clinical benefit6. Key barriers include small single-centre datasets, absent or weak external validation, inconsistent reference standards, limited calibration and explainability, and unresolved privacy, governance, and workflow questions1.

Keywordsartificial intelligencemachine learningdeep learningperiodonticsperiodontitisdiagnosisperi-implant diseaselarge language models
  1. Khubrani, Y. H., Thomas, D., Slator, P. J., White, R. D., & Farnell, D. J. J. (2024). Detection of periodontal bone loss and periodontitis from 2D dental radiographs via machine learning and deep learning: systematic review employing APPRAISE-AI and meta-analysis. Dentomaxillofacial Radiology, 54(2), 89–108. https://doi.org/10.1093/dmfr/twae070
  2. Tonetti, M. S., Greenwell, H., & Kornman, K. S. (2018). Staging and grading of periodontitis: Framework and proposal of a new classification and case definition. Journal of Periodontology, 89(S1), S159–S172. https://doi.org/10.1002/jper.18-0006
  3. Chatzopoulos, G. S., Koidou, V. P., Tsalikis, L., & Kaklamanos, E. G. (2025). Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review. Medicina, 61(6), 1066. https://doi.org/10.3390/medicina61061066
  4. Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., Dörfer, C., & Schwendicke, F. (2019). Deep Learning for the Radiographic Detection of Periodontal Bone Loss. Scientific Reports, 9(1), 8495. https://doi.org/10.1038/s41598-019-44839-3
  5. Santamaria, P., Troiano, G., Serroni, M., Araùjo, T. G., Ravidà, A., & Nibali, L. (2024). Exploring the accuracy of tooth loss prediction between a clinical periodontal prognostic system and a machine learning prognostic model. Journal of Clinical Periodontology, 51(10), 1333–1341. https://doi.org/10.1111/jcpe.14023
  6. Patil, S., Licari, F. W., Bhandi, S., & Venkataiah, V. S. (2026). AI detection of peri-implantitis on 2D radiographs: a systematic review and diagnostic accuracy assessment. Odontology. https://doi.org/10.1007/s10266-026-01412-5
  7. Li, Y., Wu, X., Liu, M., Deng, K., Tullini, A., Zhang, X., Shi, J., Lai, H., & Tonetti, M. S. (2024). Enhanced control of periodontitis by an artificial intelligence‐enabled multimodal‐sensing toothbrush and targeted mHealth micromessages: A randomized trial. Journal of Clinical Periodontology, 51(12), 1632–1643. https://doi.org/10.1111/jcpe.13987
  8. Valente, N. A., Floris, L., Cinquini, C., Natto, Z. S., & Mordini, L. (2026). Readability and Quality of Chatbot Responses to Periodontal Patient Queries: A Cross‐Sectional Evaluation of Three Publicly Accessible Large Language Models. International Journal of Dentistry, 2026(1), 3188084. https://doi.org/10.1155/ijod/3188084
  9. Yadavalli, P., Reddy, S. S. P., & Kesavalu, L. (2026). Artificial intelligence in periodontics and peri-implant medicine: from diagnosis to precision care. Frontiers in Dental Medicine, 7, 1816844. https://doi.org/10.3389/fdmed.2026.1816844
  10. Chang, H. J., Lee, S. J., Yong, T. H., Shin, N. Y., Jang, B. G., Kim, J. E., Huh, K. H., Lee, S. S., Heo, M. S., Choi, S. C., Kim, T. I., & Yi, W. J. (2020). Deep Learning Hybrid Method to Automatically Diagnose Periodontal Bone Loss and Stage Periodontitis. Scientific Reports, 10(1), 7531. https://doi.org/10.1038/s41598-020-64509-z
  11. Patil, S., Joda, T., Soffe, B., Awan, K. H., Fageeh, H. N., Tovani-Palone, M. R., & Licari, F. W. (2023). Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases. The Journal of the American Dental Association, 154(9), 795–804.e1. https://doi.org/10.1016/j.adaj.2023.05.010
  12. Turosz, N., Chęcińska, K., Chęciński, M., Brzozowska, A., Nowak, Z., & Sikora, M. (2023). Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews. Dentomaxillofacial Radiology, 52(7), 20230284. https://doi.org/10.1259/dmfr.20230284
  13. Polizzi, A., Quinzi, V., Lo Giudice, A., Marzo, G., Leonardi, R., & Isola, G. (2024). Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review. JDR Clinical & Translational Research, 9(4), 312–324. https://doi.org/10.1177/23800844241232318
  14. Feher, B., de Souza Oliveira, E. H., Mendes Duarte, P., Werdich, A. A., Giannobile, W. V., & Feres, M. (2025). Machine learning‐assisted prediction of clinical responses to periodontal treatment. Journal of Periodontology, 96(11), 1199–1212. https://doi.org/10.1002/jper.24-0737
  15. Atieh, M. A., Shah, M., Hakam, A., AlAli, F., AlSayed, S., Tawse‐Smith, A., & Alsabeeha, N. H. M. (2026). Diagnostic Accuracy of Deep Learning Models in Detecting Peri‐Implant Marginal Bone Loss: A Systematic Review and Meta‐Analysis. Clinical Oral Implants Research, 37(4), 385–401. https://doi.org/10.1111/clr.70090
  16. Li, Y., Wang, X., Zhu, H., & Ye, W. (2025). The diagnostic performance of AI based on dental radiographs in predicting marginal bone loss around dental implants: A systematic review and meta-analysis. The Journal of Prosthetic Dentistry, 134(6), 2190.e1–2190.e11. https://doi.org/10.1016/j.prosdent.2025.08.021
  17. Chatzopoulos, G. S., Koidou, V. P., Tsalikis, L., & Kaklamanos, E. G. (2025). Large language models in periodontology: Assessing their performance in clinically relevant questions. The Journal of Prosthetic Dentistry, 134(6), 2328–2336. https://doi.org/10.1016/j.prosdent.2024.10.020
  18. Koidou, V. P., Chatzopoulos, G. S., Tsalikis, L., & Kaklamanos, E. G. (2025). Large Language Models in peri-implant disease: How well do they perform? The Journal of Prosthetic Dentistry, 134(6), 2435–2442. https://doi.org/10.1016/j.prosdent.2025.02.008

Looking up citing articles…

    Copyright © 2026 Academia Journal of Medicine