Artificial Intelligence for Thyroid Nodule Characterization: Where Are We Standing?

Salvatore Sorrenti, Vincenzo Dolcetti, Maija Radzina, Maria Irene Bellini (Coresponding Author), Fabrizio Frezza, Khushboo Munir, Giorgio Grani, Cosimo Durante, Vito D'Andrea, Emanuele David, Pietro Giorgio Calò, Eleonora Lori, Vito Cantisani

Research output: Contribution to journalReview articlepeer-review

39 Citations (Scopus)
15 Downloads (Pure)


Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.

Original languageEnglish
Article number 3357
Issue number14
Publication statusPublished - 10 Jul 2022
Externally publishedYes


  • artificial intelligence
  • machine learning
  • thyroid cancer

Field of Science*

  • 3.2 Clinical medicine

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database


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