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Vol. 1 No. 2 (2024)

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November 20, 2025
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December 31, 2024

Copyright (c) 2024 saud Faisal Abed, Nada Jameel mahmad salamah, Fatin Soud ALsuhaimi, Rabab Mahdi  Mustafa Alnakhali, Masudah Zidan Aljohani, Wedyan bakor

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Artificial Intelligence in Oral and Maxillofacial Diseases: Enhancing Early Detection, Advancing Treatment Methods, and Accelerating the Prevention and Management Process.

https://doi.org/10.64483/202412255
saud Faisal Abed
Al-Awali primary health center, Ministry of Health, Saudi Arabia
Nada Jameel mahmad salamah
Alawali health center ,ministry of health, Saudi Arabia
Fatin Soud ALsuhaimi
Al-Hurra Al-Sharqiya Health Center, Ministry of Health , Saudi Arabia
Rabab Mahdi  Mustafa Alnakhali
Alia  Health CareCenter Maddinah, ministry of health , Saudi Arabia
Masudah Zidan Aljohani
Madinah Aldefaa health center, Ministry of Health , Saudi Arabia
Wedyan bakor
Al-dowaimah phcc, Ministry of Health , Saudi Arabia
Kawther Mohsen Rizq Alkathere
Al-dowaimah phcc, Ministry of Health, Saudi Arabia, Saint Barthélemy
Hajer Said Aloufi
Alhrra Alshrqia Care Center, Ministry of Health, Saudi Arabia
Mohammed Saud Saad Alsaedi
Maternity and Children hospital, Ministry of Health, Saudi Arabia

Corresponding Author(s) : saud Faisal Abed

sfabid@moh.gov.sa

Saudi Journal of Medicine and Public Health, Vol. 1 No. 2 (2024)

Abstract

Background: Oral and Maxillofacial (OMF) diseases are a significant global health challenge. The conventional approach to diagnosis, which bears subjectivity from the clinical and radiographic aspects, can delay treatment stratification. Artificial Intelligence (AI), specifically deep learning, could solve these problems by providing new levels of precision and personalization to OMF medicine.


Aim: The purpose of this review is to summarize and describe the current uses of AI in detecting, treating, and managing OMF diseases, including how machine learning can provide deeper diagnostics, personalized treatment, and predictive analyses.


Methods: A narrative review was conducted using peer-reviewed literature documenting AI in OMF medicine from PubMed, Scopus, IEEE Xplore, and Google Scholar (2018-2024).


Results: Convolutional neural networks continue to exhibit strong performance in the detection and classification of oral and maxillofacial pathologies from images, consistently performing equal to or better than clinicians in these classifications. AI-based systems provide a new level of precision in surgical treatment planning, implantology, and orthodontics. Predictive analytics provide a level of risk stratification and prognosis creation that facilitates the shift from reactionary health management to proactive health management.


Conclusion: AI has begun to reshape the practice of OMF medicine, whether it be through earlier detection, a more precise intervention, or better overall patient outcomes. Challenges do exist, as in all fields, such as real-world data standardization and clinical validation. However, the opportunities for AI to establish a standard of care based on proactive, predictive, and personalized capabilities in conjunction with human capabilities is significant.

Keywords

Artificial Intelligence, Oral Cancer Detection, Precision Dentistry, Predictive Analytics, Deep Learning.
Abed, saud F., Nada Jameel mahmad salamah, Fatin Soud ALsuhaimi, RababMahdi MustafaAlnakhali, Masudah Zidan Aljohani, Wedyan bakor, … Alsaedi, M. S. S. (2024). Artificial Intelligence in Oral and Maxillofacial Diseases: Enhancing Early Detection, Advancing Treatment Methods, and Accelerating the Prevention and Management Process. Saudi Journal of Medicine and Public Health, 1(2), 948–954. https://doi.org/10.64483/202412255
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