Maad Rayan Publishing Company Biomedical Research Bulletin 2980-9924 3 4 2025 12 29 Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis: Concepts, Challenges, and Solutions 168 192 10.34172/biomedrb.9080 EN Milad Yousefi https://orcid.org/0009-0006-5790-5144 Hadi Vahedi https://orcid.org/0009-0008-4769-451X Shadi Farabi Maleki https://orcid.org/0009-0003-6124-2599 Mahya Ahmadpour Youshanlui https://orcid.org/0000-0003-2617-0561 Aida Jafari https://orcid.org/0009-0003-3858-2886 Parisa Rostami https://orcid.org/0009-0005-3336-0402 Kais I. Abdul-Lateef Al-Abdullah https://orcid.org/0000-0002-1315-9702 Ryszard Tadeusiewicz https://orcid.org/0000-0002-4317-2801 Paweł Pławiak https://orcid.org/0000-0001-9675-5819 Roohallah Alizadehsani https://orcid.org/0000-0003-0898-5054 Siamak Pedrammehr https://orcid.org/0000-0002-2974-1801 REVIEW 10.34172/biomedrb.9080 2025 09 01 2025 09 20 Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. Ai-driven radiomics has shown great promise in improving diagnostic precision and predicting treatment outcomes. Therefore, this review examined the application of AI and radiomics to thyroid cancer diagnosis and treatment. Multiple databases, including PubMed, Medline, EMBASE, Scopus, and Web of Sciences, were reviewed until October 2024. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. Among the 42 investigated studies, radiomics analysis, incorporating ultrasound images, demonstrated its effectiveness in diagnosing thyroid cancer. Some studies presented new strategies that outperformed the status quo. The literature emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 42 included studies revealed the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, several approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer.