: Treating head and neck tumors has undergone significant advancements, focusing on improving the patient's quality of life after treatment. Reconstructive surgical techniques with free flaps have been vital in restoring anatomy, function, and aesthetics, reducing morbidity from locoregional treatments. Monitoring free flaps is crucial to detect and address any vascular compromise that may lead to flap failure. Various monitoring techniques have been employed in free flap monitoring. However, standardizing them presents a challenge due to the need for more consensus among surgeons and variability in techniques, costs, and training requirements. Artificial intelligence (AI) shows promise in standardizing monitoring practices and reducing the operator-dependent variability. AI techniques have been explored to improve monitoring and early detection of complications in free flap surgeries, and they have shown high accuracy in analyzing images and predicting flap outcomes. Despite the potential benefits, implementing AI in free flap monitoring remains challenging. Standardization of input data, interpretation, cost, training, and accounting for patient and flap variability are crucial considerations. Further research, including multicenter studies, validation, and collaboration amongst clinicians, researchers, and AI experts is needed to overcome these challenges.
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