AI Insight
This systematic review analyzed 42 studies examining the application of artificial intelligence and radiomics for thyroid cancer diagnosis, following PRISMA guidelines through October 2024. The analysis found that radiomics approaches, particularly those using ultrasound imaging, demonstrated effectiveness in diagnosing thyroid cancer, with some novel methods outperforming existing diagnostic standards. However, the review identified significant challenges including AI model interpretability issues, limited dataset availability, and dependence on operator expertise.
Why it matters
Improved AI-based diagnostic tools could enhance the accuracy and efficiency of thyroid cancer detection, potentially leading to earlier intervention and better patient outcomes. The identification of current limitations and proposed solutions through explainable AI and standardized protocols provides a roadmap for future clinical implementation of these technologies.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2024. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 368 papers were returned from the original search after 112 duplicates were removed. Relevant studies were selected according to predetermined criteria after 176 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 42 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 42 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. 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.