Systematic Reviews
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Dec 20, 2025; 15(4): 105516
Published online Dec 20, 2025. doi: 10.5662/wjm.v15.i4.105516
Artificial intelligence for early diagnosis and risk prediction of periodontal-systemic interactions: Clinical utility and future directions
Neelam Das, Keertana R Gade, Pavan K Addanki
Neelam Das, Department of Periodontology, Sri Sai College of Dental Surgery, Vikarabad 501102, Telangana, India
Keertana R Gade, Project Manager, Data Quality, Prime Healthcare Management Inc., Ontario, CA 91764, United States
Pavan K Addanki, Department of Periodontics, Kamineni Institute of Dental Sciences, Narketpally 508254, Telangana, India
Author contributions: Das N contributed to the conceptualization, methodology, data collection, manuscript drafting and writing, and key revisions of the manuscript; Gade KR contributed to the literature review of the manuscript; Addanki PK contributed to the manuscript methodological support and manuscript review; Das N and Gade KR contributed to data analysis; Das N, Gade KR, and Addanki PK performed the final approval of the manuscript; and all authors thoroughly reviewed and endorsed the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Neelam Das, Associate Professor, Department of Periodontology, Sri Sai College of Dental Surgery, 1-2-64/1&2, Kothrepally, Alampally, Vikarabad 501102, Telangana, India. dasneelam423@gmail.com
Received: January 27, 2025
Revised: March 23, 2025
Accepted: April 16, 2025
Published online: December 20, 2025
Processing time: 191 Days and 21.8 Hours
Core Tip

Core Tip: This article evaluates the impact of artificial intelligence (AI) in diagnosing and predicting periodontal-systemic interactions from 2010 to 2024. AI models integrating multi-omics data and imaging techniques like cone beam computed tomography and magnetic resonance imaging improved diagnostic accuracy (up to 92%) and reduced diagnostic time by 40%. cone beam computed tomography reduced diagnostic errors by 35%, while magnetic resonance imaging enhanced soft-tissue evaluation by 25%. AI-driven approaches improved predictive accuracy by 25%, highlighting the value of multi-omics integration and advanced imaging in enhancing precision healthcare and early disease management.