Abstract
Artificial intelligence (AI) is a promising tool for detecting oral cancer, as it is less invasive and costly, and has a high potential for accuracy. In this context, this study aimed to explore the application of AI for the early detection of oral cancer, its applications, and its future potential. To this end, a five-stage literature review was conducted in the Scopus, Scielo, and Latindex databases (2019-2024). The results revealed that AI performs effectively in the diagnosis of oral cancer, thanks to its ability to identify patterns, rapidly analyze data, perform classification tasks, and other capabilities. It was concluded that AI still faces several challenges that must be overcome; therefore, it is expected that, in the future, AI will continue to develop and be definitively integrated into oncological processes.
References
D. E. Ordóñez, A. F. Chamorro, J. A. Cruz, and M. A. Pizarro, “Evaluación del conocimiento del cáncer oral y manejo odontológico del paciente oncológico en Cali, Colombia,” Acta Odontológica Colombiana, vol. 10, no. 1, 2020, doi: 10.15446/aoc.v10n1.82933.
D. C. Cazar and A. D. C. Armas, “Etiología más frecuente del cáncer oral en adultos jóvenes: Una revisión de literatura,” Revista San Gregorio, vol. 52, pp. 175–188, 2022, doi: 10.36097/rsan.v0i52.2149.
P. A. Mena, H. L. Escobar, and K. D. Panchi, “Revisión sistemática sobre la detección temprana del cáncer oral.,” Dilemas contemporáneos: Educación, Política y Valores, vol. 10, p. 72, 2022, doi: 10.46377/dilemas.v10i18.3449.
A. J. Díaz, L. Anaya, and C. J. Rojano, “Análisis de los métodos de detección de lesiones orales potencialmente malignas,” Acta Médica Colombiana, vol. 46, no. 1, p. 1, 2020, doi: 10.36104/amc.2021.1730.
M. C. Castillo, K. J. Obispo, and J. H. Wilches, “Impacto de la inteligencia artificial en la odontología: una reflexión,” Ustasalud, vol. 23, no. 1, 2023, doi: 10.15332/us.v23i1.3142.
S. Hegde, V. Ajila, W. Zhu, and C. Zeng, “Artificial intelligence in early diagnosis and prevention of oral cancer,” Asia Pac J Oncol Nurs, vol. 9, no. 12, p. 100133, 2022, doi: 10.1016/j.apjon.2022.100133.
I. Molina-Ávila, J. M. Pimentel-Solá, A. Rocha-Buelvas, and C. A. Hidalgo-Patiño, “Cáncer Oral: Conocimiento, Actitudes y Prácticas de los Odontologos de la Provincia de Salta, Argentina, 2018,” International journal of odontostomatology, vol. 16, no. 2, pp. 249–257, 2022, doi: 10.4067/S0718-381X2022000200249.
S. Abati, C. Bramati, S. Bondi, A. Lissoni, and M. Trimarchi, “Oral Cancer and Precancer: A Narrative Review on the Relevance of Early Diagnosis,” Int J Environ Res Public Health, vol. 17, no. 24, p. 9160, 2020, doi: 10.3390/ijerph17249160.
S. L. Bermúdez, P. M. Canto, M. D. Artiles, J. R. Rodríguez, and M. D. Durán, “Citología exfoliativa en el diagnóstico precoz del cáncer bucal,” Acta Médica del Centro, vol. 15, no. 3, pp. 425–438, 2021.
D. Bastías, A. Maturana, C. Marín, R. Martínez, and S. E. Niklander, “Salivary Biomarkers for Oral Cancer Detection: An Exploratory Systematic Review,” Int J Mol Sci, vol. 25, no. 5, p. 2634, 2024, doi: 10.3390/ijms25052634.
D. H. Kim, S. W. Kim, and S. H. Hwang, “Autofluorescence imaging to identify oral malignant or premalignant lesions: Systematic review and meta‐analysis,” Head Neck, vol. 42, no. 12, pp. 3735–3743, 2020, doi: 10.1002/hed.26430.
W. Jerjes, H. Stevenson, D. Ramsay, and Z. Hamdoon, “Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence,” J Clin Med, vol. 13, no. 19, p. 5822, 2024, doi: 10.3390/jcm13195822.
A. DCruz et al., “Use of Oral Rub and Rinse Technique for Oral Cancer Screening: Results from a Community-based Program in an LMIC,” Asian Pacific Journal of Cancer Prevention, vol. 26, no. 1, pp. 91–99, 2025, doi: 10.31557/APJCP.2025.26.1.91.
T. V. Pierfelice et al., “The Diagnostic Potential of Non-Invasive Tools for Oral Cancer and Precancer: A Systematic Review,” Diagnostics, vol. 14, no. 18, p. 2033, 2024, doi: 10.3390/diagnostics14182033.
C. S. Chu, N. P. Lee, J. Adeoye, P. Thomson, and S. Choi, “Machine learning and treatment outcome prediction for oral cancer,” Journal of Oral Pathology & Medicine, vol. 49, no. 10, pp. 977–985, 2020, doi: 10.1111/jop.13089.
D. K. Das, S. Bose, A. K. Maiti, B. Mitra, G. Mukherjee, and P. K. Dutta, “Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis,” Tissue Cell, vol. 53, pp. 111–119, 2019, doi: 10.1016/j.tice.2018.06.004.
E. Duran-Sierra et al., “Machine-Learning Assisted Discrimination of Precancerous and Cancerous from Healthy Oral Tissue Based on Multispectral Autofluorescence Lifetime Imaging Endoscopy,” Cancers (Basel), vol. 13, no. 19, p. 4751, 2021, doi: 10.3390/cancers13194751.
A. Alhazmi et al., “Application of artificial intelligence and machine learning for prediction of oral cancer risk,” Journal of Oral Pathology & Medicine, vol. 50, no. 5, pp. 444–450, 2021, doi: 10.1111/jop.13157.
M. P. Kirubabai and G. Arumugam, “Deep Learning Classification Method to Detect and Diagnose the Cancer Regions in Oral MRI Images,” Medico-Legal Update, vol. 21, no. 1, pp. 462–468, 2021, doi: 10.37506/mlu.v21i1.2353.
S. Xu et al., “An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 158603–158611, 2019, doi: 10.1109/ACCESS.2019.2950286.
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