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Life sciences · Journal article
Frontiers in Endocrinology · November 4, 2026
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Thyroid cancer is one of the most common endocrine malignancies, and its rising incidence has increased the demand for more accurate and efficient diagnostic and management strategies. Current clinical management mainly relies on ultrasonography, fine-needle aspiration (FNA), surgery, radioactive iodine therapy, thyroid-stimulating hormone suppression, and systemic treatment in selected advanced cases. However, substantial interobserver variability, indeterminate cytology, and heterogeneity in tumor behavior still complicate diagnosis and risk stratification. In thyroid cancer, AI(Artificial intelligence) -assisted models have been applied to ultrasound, CT, MRI, cytology, histopathology, and genomic data, with promising performance in distinguishing benign from malignant nodules, predicting lymph node metastasis and key mutations, and supporting individualized management. This review critically synthesizes evidence across these applications, focusing on study design, validation strategies, generalizability, and clinical relevance. It also examines explainable AI, emerging foundation models, and regulatory and implementation challenges. Although many models report high diagnostic or predictive performance, these results alone do not establish improvements in clinical decision-making or patient outcomes. Prospective clinical validation, cost-effectiveness assessment, and evaluation of integration into clinical workflows are needed to determine whether AI provides meaningful benefits over standard care.