  The growing interest in developing smart diagnostic systems to help medical
experts process extensive data for treating incurable diseases has been
notable. In particular, the challenge of identifying thyroid cancer (TC) has
seen progress with the use of machine learning (ML) and big data analysis,
incorporating transformers to evaluate TC prognosis and determine the risk of
malignancy in individuals. This review article presents a summary of various
studies on AIbased approaches, especially those employing transformers, for
diagnosing TC. It introduces a new categorization system for these methods
based on artifcial intelligence (AI) algorithms, the goals of the framework,
and the computing environments used. Additionally, it scrutinizes and contrasts
the available TC datasets by their features. The paper highlights the
importance of AI instruments in aiding the diagnosis and treatment of TC
through supervised, unsupervised, or mixed approaches, with a special focus on
the ongoing importance of transformers in medical diagnostics and disease
management. It further discusses the progress made and the continuing obstacles
in this area. Lastly, it explores future directions and focuses within this
research feld.
