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
Abstract
BACKGROUND

Artificial intelligence (AI) is transforming healthcare by improving diagnostic accuracy and predictive analytics. Periodontal diseases are recognized as risk factors for systemic conditions, including type 2 diabetes mellitus, cardiovascular disease, Alzheimer’s disease, polycystic ovary syndrome, thyroid dysfunction, and post-coronavirus disease 2019 complications. These conditions exhibit complex bidirectional interactions, underscoring the importance of early detection and risk stratification. Current diagnostic tools often fail to capture these interactions at an early stage, limiting timely intervention. This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions, enhancing clinical outcomes.

AIM

To evaluate AI’s role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.

METHODS

This systematic review followed PRISMA guidelines (2009) and included peer-reviewed articles from PubMed, Scopus, and Embase. Studies with large sample sizes (≥ 500 participants) were selected, focusing on AI models integrating multi-omics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging. Machine learning models processed structured clinical data, deep learning models combined imaging and clinical data, and natural language processing models extracted insights from clinical notes.

RESULTS

AI applications significantly enhanced diagnostic and predictive accuracy, reducing diagnostic time by 40% and improving predictive accuracy by 25% in periodontal patients with type 2 diabetes mellitus. Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%, with specificity and sensitivity rates of 94% and 90%, respectively. Increasing sample sizes over the years reflected advancements in AI, data collection, and model training, reinforcing model reliability.

CONCLUSION

AI’s integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions, improving clinical outcomes and decision-making.

Keywords: Artificial intelligence; Early diagnosis; Risk prediction; Periodontal-systemic interactions; Type 2 diabetes mellitus; Hypertension; Pancreatic cancer; Artificial intelligence in healthcare; Systematic review

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.