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Habchi, Yassine, Kheddar, Hamza, Himeur, Yassine and Ghanem, Mohamed Chahine (2026) Machine learning and transformers for thyroid carcinoma diagnosis. Journal of Visual Communication and Image Representation, 115 (104668). pp. 1-33. ISSN 1047-3203
Thyroid carcinoma (TC) remains a critical health challenge, where timely and accurate diagnosis is essential for improving patient outcomes. This review provides a comprehensive examination of artificial intelligence (AI) applications — including machine learning (ML), deep learning (DL), and emerging transformer-based approaches — in the detection and classification of TC. We first outline standardized evaluation metrics and analyze publicly available datasets, highlighting their limitations in diversity, annotation quality, and representativeness. Next, we survey AI-driven diagnostic frameworks across three domains: classification, segmentation, and prediction, with emphasis on ultrasound imaging, histopathology, and genomics. A comparative analysis of ML and DL approaches illustrates their respective strengths, such as interpretability in smaller datasets versus automated feature extraction in large-scale imaging tasks. Advanced methods leveraging vision transformers (ViT) and large language models (LLMs) are discussed alongside traditional models, situating them within a broader ecosystem of feature engineering, ensemble learning, and hybrid strategies. We also examine key challenges — imbalanced datasets, computational demands, model generalizability, and ethical concerns — before outlining future research directions, including explainable AI, federated and privacy-preserving learning, reinforcement learning, and integration with the Internet of Medical Things (IoMT). By bridging technical insights with clinical considerations, this review establishes a roadmap for next-generation TC diagnostics and highlights pathways toward robust, patient-centric, and ethically responsible AI deployment in oncology.
Available under License Creative Commons Attribution 4.0.
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