  There has been a growing interest in creating intelligent diagnostic systems
to assist medical professionals in analyzing and processing big data for the
treatment of incurable diseases. One of the key challenges in this field is
detecting thyroid cancer, where advancements have been made using machine
learning (ML) and big data analytics to evaluate thyroid cancer prognosis and
determine a patient's risk of malignancy. This review paper summarizes a large
collection of articles related to artificial intelligence (AI)-based techniques
used in the diagnosis of thyroid cancer. Accordingly, a new classification was
introduced to classify these techniques based on the AI algorithms used, the
purpose of the framework, and the computing platforms used. Additionally, this
study compares existing thyroid cancer datasets based on their features. The
focus of this study is on how AI-based tools can support the diagnosis and
treatment of thyroid cancer, through supervised, unsupervised, or hybrid
techniques. It also highlights the progress made and the unresolved challenges
in this field. Finally, the future trends and areas of focus in this field are
discussed.
