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

Simple Summary In the present review, an up-to-date summary of the state of the art of artificial intelligence (AI) implementation for thyroid nodule characterization and cancer is provided. The opinion on the real effectiveness of AI systems remains controversial. Taking into consideration the largest and most scientifically valid studies, it is possible to state that AI provides results that are comparable or inferior to expert ultrasound specialists and radiologists. Promising data approve AI as a support tool and simultaneously highlight the need for a radiologist supervisory framework for AI provided results. Therefore, current solutions might be more suitable for educational purposes. 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.

Automated summary

This article provides an up-to-date summary of the current state and controversies surrounding the implementation of artificial intelligence (AI) for characterizing thyroid nodules and cancer. While AI has potential benefits in improving diagnostic accuracy and reducing inter-observer variability, there is still ongoing debate about its real effectiveness compared to expert clinicians. Current evidence suggests that AI systems provide results comparable or slightly inferior to ultrasound specialists and radiologists. However, AI can be a valuable support tool, particularly in settings with limited expertise, and it may have potential for training purposes.