AI in Thyroid Nodule Imaging: From Detection to Clinical Integration

Artificial intelligence for thyroid nodule imaging has evolved from simple classifiers to comprehensive systems handling detection, segmentation, and risk stratification, now aligning with biopsy decision pathways and clinical workflows.

• Current AI methods leverage transformer architectures, multimodal ultrasound inputs, and self-supervised pretraining to improve consistency across diverse settings.
• Algorithmic outputs are being harmonized with radiologist decision-making to improve reporting consistency and streamline management recommendations.
• The field is shifting focus toward practical deployment, interpretability, and sustained clinical impact rather than technical performance alone.

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