AI and Imaging for Oral Cancer Diagnosis and Prognosis

The integration of artificial intelligence (AI) with imaging has emerged as a transformative force in the field of oral oncology. Oral cancer remains a significant global health burden, often associated with late diagnoses and suboptimal prognostic assessment, which impede timely intervention and reduce survival rates. Recent advancements in imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and digital pathology, combined with machine learning and deep learning algorithms has shown great promise in improving the detection, characterization, risk stratification, and outcome prediction of oral malignancies.Despite these advanced, challenges persist regarding the generalizability, interpretability, and standardization of AI models across heterogeneous datasets and diverse patient populations. Debates continue over the optimal integration of AI into clinical workflows and whether AI models can consistently outperform human expertise in clinical context. While several studies have demonstrated enhanced sensitivity and specificity using AI-assisted imaging, consensus on best practices, real-world validation and regulatory frameworks remain limited, highlighting the need for robust, collaborative, and translational research in this field.This Research Topic aims to explore the integration of AI and imaging modalities in enhancing the diagnosis, prognosis, and management of oral cancer and precancer. By combining computational modeling with radiologic, histopathologic, optical and spectroscopic imaging, researchers can develop innovative algorithms and predictive models that support clinicians in early detection, grading, and outcome prediction. Areas of interest include deep learning for lesion segmentation and classification, radiomic and pathomic feature extraction for prognostic modeling, and explainable AI (XAI) that facilitate clinical interpretability and trust. Furthermore, the topic also encourages the exploration of multimodal data fusion, federated learning for multi-institutional collaboration, and real-time chairside diagnostic tools using optical imaging and AI. We invite original research articles, reviews, and methodological papers that advance the use of AI and imaging in the diagnosis and prognosis of oral cancer, fostering translational research toward precision oral oncology. Potential subtopics include:• Development and validation of AI algorithms for detection and classification of oral cancer and precancerous lesions.• Integration of imaging techniques with AI for predicting treatment response and survival.• Application of deep learning in digital pathology for tissue analysis.• Explainable and transparent AI for enhanced clinical interpretability and trust.• Synthetic data generation and augmentation techniques for oral oncology.• Comparative studies evaluating AI performance versus traditional diagnostic approaches.• Assessment of AI model generalizability and robustness across diverse populations and imaging platforms.• Future prospects, translational challenges and regulatory considerations for AI-powered oral oncology. Topic editors: Rong (Rose) Wang, Freny Karjodkar Source: https://www.frontiersin.org/research-topics/75790/ai-and-imaging-for-oral-cancer-diagnosis-and-prognosis