Artificial Intelligence for Thyroid Ultrasound: Clinical Performance, Pitfalls, and Practice Integration

Abstract

The use of artificial intelligence (AI) in thyroid ultrasound is bringing important changes to endocrine imaging, helping improve diagnostic accuracy and make the assessment of thyroid nodules more consistent. This review examines the current applications, technological approaches, clinical performance, adversities, and future directions of AI-based systems in thyroid ultrasound. Recent studies suggest that AI technologies hold significant potential in thyroid ultrasound, particularly in automated nodule detection, classification, and risk stratification. Deep learning models, particularly convolutional neural networks, achieve diagnostic accuracies exceeding 90% in distinguishing benign from malignant nodules, often matching or surpassing human radiologist performance. Current applications include Thyroid Imaging Reporting and Data System-based classification systems, lymph node metastasis prediction, and real-time diagnostic assistance. However, challenges including reproducibility concerns, clinical workflow integration, and regulatory considerations remain significant barriers to widespread adoption. While AI shows remarkable promise in thyroid ultrasound applications, challenges including validation requirements, standardization needs, and clinical integration barriers must be addressed for widespread adoption. Future developments should focus on multimodal integration, explainable AI systems, and prospective clinical trials to fully utilize the potential of AI in transforming thyroid diagnostics.

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