EventsMOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published
This submission belongs to the session 05. USEDAT.NET: USA-Europe Data Analysis Trends & Complex Networks Mini Congress Series, Coruña, SP-Miami, USA, 2023 of the event MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed.
Published date
22 Dec, 2023
Academic Editor
author-avatarHumbert G. Díaz
Citation
Prabhu Manickam Natarajan, Saif Khaled Samih Abdelaziz, The Role of Artificial Intelligence in Periodontics, in Proceedings of MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed., 25 December–31 December 2023, MDPI: Basel, Switzerland
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The Role of Artificial Intelligence in Periodontics

Saif Khaled Samih Abdelaziz 2
1. Department of Clinical Sciences, Centre of Medical and Bio-Allied Health Sciences and Research, Ajman University, Ajman P.O. Box 346, United Arab Emirates., United Arab Emirates
2. Fourth Year Student in the College of Dentistry, Ajman University, United Arab Emirates
Abstract

Periodontics, as a specialized field in dentistry, plays a pivotal role in the maintenance of oral health by focusing on the prevention, diagnosis, and treatment of periodontal diseases. In recent years, the integration of Artificial Intelligence (AI) has emerged as a transformative force, promising advancements in diagnostics, treatment planning, and patient management within the realm of periodontics. This review aims to explore and evaluate the current state of AI applications in Periodontics, examining its potential impact on clinical practice, research, and education.

The review begins by elucidating the fundamental concepts of AI and its various subfields, such as machine learning and deep learning, that contribute to the development of intelligent systems. Subsequently, an in-depth analysis is conducted to highlight the diverse applications of AI in Periodontics, ranging from image analysis for radiographic interpretation to predictive modeling for treatment outcomes. The discussion also addresses the challenges and limitations inherent in the current AI implementations, including issues related to data privacy, interpretability, and ethical considerations.

Furthermore, the review investigates the integration of AI-driven technologies into periodontal research, emphasizing the role of big data analytics and computational modeling in enhancing our understanding of disease mechanisms and treatment responses. It explores how AI can contribute to the personalization of treatment plans, allowing for more tailored and efficient interventions based on individual patient profiles.

The critical assessment also sheds light on the educational aspects of AI in Periodontics, discussing the potential role of AI in training programs, simulation exercises, and virtual patient scenarios. The review concludes by outlining future prospects and recommendations for the responsible and effective incorporation of AI in periodontal practice, emphasizing the need for interdisciplinary collaboration, ongoing research, and ethical considerations to harness the full potential of AI while ensuring patient safety and well-being. Overall, this review serves as a comprehensive guide for dental professionals, educators, and researchers seeking to navigate the evolving landscape of AI in the field of Periodontics.

Keywords
Periodontics
Artificial Intelligence (AI)
Machine Learning
Diagnostic Imaging
Predictive Modeling
Patient Management
Computational Modeling
Personalized Treatment
Virtual Patient Scenarios
Manuscript
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