# The impact of artificial intelligence on periodontal disease detection and treatment

## TL;DR

This paper reviews how artificial intelligence can help detect and treat periodontal disease, improving diagnosis and patient outcomes.

## Contribution

The paper provides an updated review of AI applications in periodontal disease diagnosis and treatment, emphasizing recent advancements in machine learning and predictive modeling.

## Key findings

- AI technologies like deep learning models improve diagnostic accuracy in periodontal disease.
- AI can predict disease progression and support personalized treatment planning.
- Challenges include ethical issues and data privacy in implementing AI in dental practice.

## 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.