Accelerating the Radiology Learning Curve: How the Parameter-Based I-TIRADS Lexicon Enhances Interpretive Reliability and Learning Curves Across Resident Experience Levels—A Dynamic Video-Based Study

Background/Objectives: Standardizing thyroid nodule assessment remains a major challenge in clinical practice and radiology education. Traditional categorical risk stratification systems (RSSs) often yield high interobserver variability, particularly among trainees. This study evaluated the reproducibility, educational utility, and experience-dependent performance of the parameter-based International Thyroid Imaging Reporting and Data System (I-TIRADS) lexicon compared to established categorical RSSs (ACR, EU, and K-TIRADS).