Artificial intelligence in thyroid ultrasound: clinical applications and perspectives

Abstract

Thyroid nodules are highly prevalent, with increasing detection rates driven by advanced imaging and expanded screening. Ultrasound serves as the first-line tool for screening, diagnosis and follow-up, owing to its non-invasiveness, real-time capability, cost-effectiveness and absence of ionizing radiation. However, conventional ultrasound diagnosis is highly operator-dependent, resulting in substantial inter-observer variability and diagnostic errors, particularly for subtle or indeterminate lesions. Artificial intelligence (AI), particularly deep learning and radiomics, has emerged as a promising approach to address these limitations by enabling automated feature extraction, quantitative analysis and standardized interpretation, which has the potential to improve diagnostic efficiency and risk stratification. This review summarizes AI applications in thyroid ultrasound, including image preprocessing, nodule segmentation, quantitative feature analysis, benign-malignant differentiation, TIRADS optimization and automated reporting. We highlight AI’s potential in enhancing diagnostic consistency and accuracy, while critically assessing the methodological quality, bias risks and external validation of existing studies. Most AI tools are still in early translational phases, lacking large-scale validation in real clinical settings and standardized reporting protocols. We further discuss key challenges, including data bias, limited generalizability due to small or single-center datasets, poor interpretability and significant translational barriers. Future directions involving multi-modal fusion, explainable AI, real-time clinical systems and rigorous, multi-center standardized validation are proposed to facilitate clinical translation and improve patient care.