Vol. 10, Issue 4, Part C (2024)

Oral carcinoma remains a critical global health challenge, marked by high mortality rates and poor survival outcomes, largely due to late-stage diagnoses. Early detection is paramount for improving survival rates, yet current diagnostic methods—such as conventional oral examinations and biopsies—are often hindered by clinical variability and limited access, particularly in underserved regions. Artificial intelligence (AI)-driven image segmentation, leveraging advanced deep learning techniques like Convolutional Neural Networks (CNNs), holds transformative potential to revolutionize oral carcinoma detection. By analyzing medical imaging modalities, such as radiographs, histopathology slides, and endoscopic images, AI models can accurately identify early signs of malignancy, predict carcinoma progression, and assist in precise staging—enabling earlier, life-saving interventions. However, challenges persist in ensuring data quality, managing image variability, and achieving interpretability, especially in resource-constrained healthcare environments. Recent advancements have concentrated on combining AI with smartphone imaging systems, providing an affordable and accessible solution for early diagnosis. Despite the promise, issues related to image resolution, variability, and model robustness need to be addressed. This review consolidates the potential of AI-powered image segmentation in oral radiology, focusing on its ability to enhance diagnostic accuracy, accelerate early detection, and bridge healthcare disparities, ensuring that both urban and rural populations benefit from more equitable, timely, and effective oral carcinoma care.