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Volume 17, Issue 1 (March-2025 2025)
Iranian Journal of Blood and Cancer 2025, 17(1): 47-63 |
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Issrani R, Zeeshan H M, Qamar N, Iqbal A. Exploring the Intersection of Artificial Intelligence and Oral Cancer: Diagnostic Advances, Genetic Insights, and Precision Medicine. Iranian Journal of Blood and Cancer 2025; 17 (1) :47-63
URL: http://ijbc.ir/article-1-1685-en.html
URL: http://ijbc.ir/article-1-1685-en.html
1- Department of Preventive Dentistry, College of Dentistry, Jouf University, Sakaka, Kingdom of Saudi Arabia. & Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India. , dr.rakhi.issrani@jodent.org
2- Department of Computer Science, Superior University, Lahore, Pakistan.
3- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
4- Central Library, Prince Sultan University, Rafha Street, Riyadh, Kingdom of Saudi Arabia
2- Department of Computer Science, Superior University, Lahore, Pakistan.
3- Department of Software Engineering, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
4- Central Library, Prince Sultan University, Rafha Street, Riyadh, Kingdom of Saudi Arabia
Abstract: (212 Views)
Background: Oral cancer poses a serious global health challenge due to its high morbidity and mortality rates, largely stemming from late-stage diagnosis and limited treatment success. Recent technological advances, particularly in artificial intelligence (AI), have opened new avenues for early detection and personalized treatment approaches. Objectives: This review aims to explore the role of AI, including machine learning (ML) and deep learning (DL), in the diagnosis, prognosis, and management of oral cancer. It also examines the systemic effects of oral cancer, underlying genetic and hormonal influences, and the impact of oxidative stress and chronic inflammation on disease progression.
Methodology: A systematic literature review was conducted covering publications from 1997 to 2024, using PubMed, Scopus, and the Cochrane Library. Studies involving AI, ML, and DL in oral cancer detection and treatment were selected based on predefined inclusion and exclusion criteria. Bibliometric and trend analyses were also performed to assess global research output and collaborative networks.
Results: AI techniques such as convolutional neural networks and support vector machines have demonstrated significant utility in early detection, histopathological analysis, and survival prediction. The review also highlights key genetic mutations (e.g., TP53, CDKN2A) and hormonal imbalances (e.g., estrogen, androgen receptors) linked to oral cancer pathogenesis. Furthermore, systemic involvement of organs like the liver, brain, and bone is discussed. Bibliometric data indicate increasing global collaboration and the emergence of AI as a dominant research focus in oral oncology.
Conclusion: AI-based diagnostic tools and predictive models offer promising pathways for early detection and personalized treatment in oral cancer. Understanding the molecular, systemic, and epidemiological dimensions of the disease, alongside leveraging computational advancements, can significantly enhance patient outcomes and support the development of precision medicine in oral oncology.
Methodology: A systematic literature review was conducted covering publications from 1997 to 2024, using PubMed, Scopus, and the Cochrane Library. Studies involving AI, ML, and DL in oral cancer detection and treatment were selected based on predefined inclusion and exclusion criteria. Bibliometric and trend analyses were also performed to assess global research output and collaborative networks.
Results: AI techniques such as convolutional neural networks and support vector machines have demonstrated significant utility in early detection, histopathological analysis, and survival prediction. The review also highlights key genetic mutations (e.g., TP53, CDKN2A) and hormonal imbalances (e.g., estrogen, androgen receptors) linked to oral cancer pathogenesis. Furthermore, systemic involvement of organs like the liver, brain, and bone is discussed. Bibliometric data indicate increasing global collaboration and the emergence of AI as a dominant research focus in oral oncology.
Conclusion: AI-based diagnostic tools and predictive models offer promising pathways for early detection and personalized treatment in oral cancer. Understanding the molecular, systemic, and epidemiological dimensions of the disease, alongside leveraging computational advancements, can significantly enhance patient outcomes and support the development of precision medicine in oral oncology.
Keywords: Oral Cancer, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks, Personalized Medicine
: Review Article |
Subject:
AI in Medicine
Received: 2025/02/2 | Accepted: 2025/03/3 | Published: 2025/03/30
Received: 2025/02/2 | Accepted: 2025/03/3 | Published: 2025/03/30
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