Diagnostic Performance of AI-Assisted Ultrasound in Thyroid Nodule Detection in China

Abstract

The widespread use of high-resolution ultrasound has led to a marked increase in the detection of thyroid nodules in China, making ultrasound a cornerstone of initial evaluation and risk assessment. However, conventional thyroid ultrasound is highly dependent on operator experience and subjective interpretation of sonographic features, which can result in variability in diagnostic performance across different clinical settings. These challenges are particularly evident in high-volume practices and resource-limited environments, where consistency and efficiency are difficult to maintain. In this context, artificial intelligence–assisted ultrasound has emerged as a supportive technology designed to enhance standardization and reliability in thyroid nodule detection. This paper presents a conceptual evaluation of the diagnostic performance of AI-assisted ultrasound within the Chinese healthcare system, focusing on its potential influence on sensitivity, specificity, diagnostic consistency, and clinical decision-making. Rather than reporting original empirical data, the discussion examines how AI-assisted tools may reduce inter-operator variability, support clinicians with differing levels of experience, and integrate into existing clinical workflows across primary care, tertiary hospitals, and large-scale screening programs. Challenges related to model generalizability, data quality, interpretability, and ethical considerations are also addressed. By situating AI-assisted ultrasound within real-world clinical practice, this paper aims to clarify its potential role, limitations, and future significance in thyroid nodule management in China.

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