The present study by Pozdeyev et al. reviews the expanding role of artificial intelligence (AI) in thyroid cancer care, focusing on the different methods being applied across diagnosis and management. Machine learning and deep learning models trained on ultrasound imaging, cytopathology slides, digital pathology specimens, and structured clinical data have each been tested with encouraging performance. Ultrasound-based computer vision approaches use convolutional neural networks to assess thyroid nodules with accuracy rates that often exceed those of experienced radiologists, while also reducing operator-dependent variability. In cytopathology, models trained to interpret fine-needle aspiration samples have yielded promising data in reducing indeterminate results, and digital pathology algorithms extend this analysis to whole-slide histology for malignancy detection and grading. Predictive models which incorporate clinical and molecular data provide additional tools for stratifying recurrence risk or identifying lymph node metastasis, although their external validation remains limited. More recently, natural language processing and large language models have been developed to analyze electronic health records and provide clinical decision support, with early research showing potential in documentation automation and guiding patient communication. Collectively, these methods demonstrate a trajectory toward greater diagnostic accuracy, consistency, and efficiency in thyroid cancer care.