Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years,
offering advanced tools and methodologies that promise to revolutionize patient outcomes …

Objective Thyroid pathology has great potential for automated/artificial intelligence algorithm
application as the incidence of thyroid nodules is increasing and the indeterminate …

Background The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC)
comprises 6 categories used for the diagnosis of thyroid fine‐needle aspiration biopsy …

Objective: Fine needle aspiration cytology has higher sensitivity and predictive value for
diagnosis of thyroid nodules than any other single diagnostic methods. In the Bethesda …

Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma
thyroid is a moderately sensitive and specific modality. The present machine learning tools …

Background Several studies have used artificial intelligence (AI) to analyze cytology images,
but AI has yet to be adopted in clinical practice. The objective of this study was to …

We consider machine-learning-based thyroid-malignancy prediction from cytopathology
whole-slide images (WSI). Multiple instance learning (MIL) approaches, typically used for …

Thyroid cancers (TC) have increasingly been detected following advances in diagnostic
methods. Risk stratification guided by refined information becomes a crucial step toward the …

Simple Summary Artificial intelligence (AI) has attracted significant interest in the healthcare
sector due to its promising results. Cytological examination is a critical step in the initial …

Background: From cell phones to aerospace, artificial intelligence (AI) has wide-reaching
influence in the modern age. In this review, we discuss the application of AI solutions to an …