Deep Learning and AI in Detection of Oral Malignancy - Sapkal, Rashmi; Supnekar, Shraddha; Shete, Anagha; Prakash Channe, Pallavi; Desai, Ashwini (Journal of Neonatal Surgery, 2025)
Oral cancer, particularly oral squamous cell carcinoma (OSCC), remains one of the most prevalent and deadly cancers worldwide. Early detection is critical for improving prognosis and survival rates, but traditional diagnostic methods often face challenges in identifying malignancies at early stages. Recent advancements in artificial intelligence (AI) and deep learning (DL) offer promising solutions for revolutionizing oral cancer detection. This paper explores the application of AI, specifically deep convolutional neural networks (DCNNs), in detecting oral malignancies using medical imaging, histopathological data, and clinical parameters. We review existing literature, highlighting the performance of various AI models in comparison to traditional diagnostic techniques, and provide a detailed methodology for the development and evaluation of these models. The results demonstrate that AI-based approaches significantly improve diagnostic accuracy, sensitivity, and early-stage detection of oral cancers. However, challenges remain in the integration of AI systems into clinical workflows, addressing ethical concerns, and ensuring model generalizability. Future research should focus on overcoming these barriers, improving model transparency, and exploring the integration of multimodal data to enhance diagnostic performance. This study underscores the transformative potential of AI in enhancing oral cancer detection and offers directions for future clinical applications and research.