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.

翻译：近年来，创建智能诊断系统以辅助医疗专业人员分析和处理大数据来治疗难治性疾病的兴趣日益增长。该领域的关键挑战之一是甲状腺癌的检测，而利用机器学习和大数据分析评估甲状腺癌预后并确定患者恶性风险已取得进展。本文综述了与基于人工智能的甲状腺癌诊断技术相关的大量文献。据此，根据所用的人工智能算法、框架目标以及计算平台，提出了一种新的分类方法，对这些技术进行分类。此外，本研究根据特征比较了现有的甲状腺癌数据集。重点在于基于人工智能的工具如何通过监督、无监督或混合技术，支持甲状腺癌的诊断和治疗。同时还指出了该领域取得的进展和未解决的挑战。最后，讨论了该领域的未来趋势和重点关注方向。