Modern Thyroid Cancer Diagnosis: A Review of AI-Powered Algorithms for Detection and Classification

Abstract

Thyroid nodules are one of the most common abnormalities in the thyroid gland, which are often harmless in nature (benign), but in a few unfortunate instances, they may be fatal (malignant). This review explores recent advancements in artificial intelligence (AI) applied to thyroid cancer detection and classification, with a focus on machine learning, deep learning, and image processing techniques. We provide a comprehensive evaluation of AI applications across key imaging modalities—Ultrasonography (USG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET/CT)—as well as cytopathological analysis using Fine Needle Aspiration Biopsy (FNAB). By critically examining studies on AI-driven preoperative assessments, we highlight improvements in diagnostic accuracy, sensitivity, specificity and efficiency. This review also identifies current limitations in AI applications, including technical challenges and unresolved issues that hinder widespread clinical adoption. Although significant progress has been achieved, the integration of AI in clinical settings remains limited, as AI-based outputs currently serve as supportive tools rather than definitive diagnostic evidence. We discuss the potential of AI to transform thyroid cancer diagnostics by enhancing reliability and accessibility, while addressing the need for further research to develop a unified, robust and clinically trustworthy AI framework for thyroid cancer diagnosis.