Harnessing artificial intelligence role in oral cancer diagnosis and prediction: A comprehensive exploration

Abstract

AI in oral cancer treatment comes along with advanced diagnostics and prognostics of AI to cure oral cancer better. The conventional approach which is executed via histopathological examination using visualization methods is prone to biases and withholdings. AI-based techniques such as SVM, CNN and capsule networks have brought the journey of tumor grading, stagewise cancer and further cancer detection at an early stage as a result of which, delays in treatment are reduced. The systems involving collaboration pathologists witness higher abnormality detection and treatment planning, thereby bettering the diagnosis precision and the quality of care. Deep learning techniques, particularly neural networks are an important aspect of oral cancer early detection, and they drastically minimize human error. A combination of two feature extraction techniques, hybrid and boosting algorithms, this way leads to saving time for evaluation. Hence, temporal delays in the treatment and progression of the disease are minimized. Tightly following the quality check requirements makes the trust in the AI model. AI-driven diagnostics and prognostics are considered a game-changing approach to the future of oncology treatment due to the development of standardized tools and multidisciplinary collaboration, respectively. The areas in need of refinement or expansion include research, data interoperability, and global partnerships. Such expansion will be beneficial in pursuit of the common goal of better health care supported by global cooperation.

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