From Bench-to-Bedside: How Artificial Intelligence Is Changing Thyroid Nodule Diagnostics

Abstract

Context: Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice.

Evidence acquisition: A literature search of PubMed and IEEE Xplore was conducted for English language publications between January 1, 2015 and January 1, 2023 studying diagnostic tests on suspected thyroid nodules that utilized AI. We excluded articles without prospective or external validation, non-primary literature, duplicates, focused on non-nodular thyroid conditions, not using AI, and those incidentally utilizing AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence.

Evidence synthesis: A total of 61 studies were identified; all performed external validation, sixteen studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding five high-level outcomes: (1) nodule localization, (2) ultrasound risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from ultrasound and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and inter-observer variability.

Conclusions: Models predominantly used ultrasound images to predict malignancy. Of four FDA-approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and re-validation to ensure appropriate clinical performance.

Keywords: artificial intelligence; diagnostics; machine learning; thyroid nodules.