TY - JOUR
T1 - Artificial Intelligence in Oral Cancer
T2 - A Comprehensive Scoping Review of Diagnostic and Prognostic Applications
AU - Vinay, Vineet
AU - Jodalli, Praveen
AU - Chavan, Mahesh S.
AU - Buddhikot, Chaitanya S.
AU - Luke, Alexander Maniangat
AU - Ingafou, Mohamed Saleh Hamad
AU - Reda, Rodolfo
AU - Pawar, Ajinkya M.
AU - Testarelli, Luca
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/2
Y1 - 2025/2
N2 - Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.
AB - Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.
UR - https://www.scopus.com/pages/publications/85217753146
UR - https://www.scopus.com/inward/citedby.url?scp=85217753146&partnerID=8YFLogxK
U2 - 10.3390/diagnostics15030280
DO - 10.3390/diagnostics15030280
M3 - Review article
AN - SCOPUS:85217753146
SN - 2075-4418
VL - 15
JO - Diagnostics
JF - Diagnostics
IS - 3
M1 - 280
ER -
