Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives

Abstract

Artificial intelligence (AI) has emerged as a promising tool in oral oncology, particularly in the field of prediction. This review provides a comprehensive outlook on the role of AI in predicting oral cancer, covering key aspects such as data collection and preprocessing, machine learning techniques, performance evaluation and validation, challenges, future prospects, and implications for clinical practice. Various AI algorithms, including supervised learning, unsupervised learning, and deep learning approaches, have been discussed in the context of oral cancer prediction. Additionally, challenges such as interpretability, data accessibility, regulatory compliance, and legal implications are addressed along with future research directions and the potential impact of AI on oral oncology care.

Keywords

• Artificial intelligence
• Oral oncology
• Prediction
• Machine learning
• Deep learning
• Data preprocessing