Abstract
Artificial intelligence has rapidly emerged as one of the most transformative technologies in modern medicine, fundamentally changing diagnostic workflows, surgical planning, intraoperative navigation, and postoperative patient management. In otorhinolaryngology-head and neck surgery, AI algorithms increasingly support clinicians in interpreting endoscopic images, radiological examinations, histopathological specimens, voice analysis, and audiological data. Machine learning, deep learning, computer vision, natural language processing, and generative AI models have demonstrated substantial improvements in diagnostic accuracy, workflow efficiency, and clinical decision-making. Furthermore, AI-assisted surgical navigation, robotic surgery, and predictive analytics contribute to enhanced surgical precision while reducing complications and improving patient outcomes. Despite these advances, several challenges remain, including limited external validation, algorithmic bias, data privacy concerns, regulatory uncertainties, and the need for explainable AI systems suitable for clinical implementation. Future developments are expected to integrate multimodal AI, digital twins, real-time surgical guidance, and personalized precision medicine into routine otorhinolaryngological practice. This review summarizes the current applications of artificial intelligence in otorhinolaryngology surgery, discusses existing limitations, and highlights future perspectives for safe and effective clinical integration.