Artificial Intelligence for Real Time Resection Margin Evaluation in Oral Cancer: A Future Perspective - Mukul, Sailesh Kumar; Rashid, Sibgutulah; Gupta, Vyakhya Akhileshkumar; Kalimuddin, Khan Sabera (Journal of Orofacial Research, 2025)
Clear resection margins in oral cancer surgery are essential for minimizing local recurrence and enhancing patient outcomes. Conventional intraoperative margin assessment, primarily through frozen section analysis, is time intensive and prone to sampling errors. Artificial intelligence (AI) offers a promising solution for real time, accurate margin evaluation. This review examines AI's role in intraoperative margin assessment for oral cancer, focusing on machine learning (ML), deep learning (DL), and imaging based techniques such as hyperspectral imaging (HSI), optical coherence tomography (OCT), and Raman spectroscopy. We review their applications, advantages, limitations, and future potential, supported by scientific evidence. AI driven methods improve precision, reduce operative time, and enhance oncologic outcomes, paving the way for transformative advancements in surgical practice.