Sep 2026· Journal of imaging informatics in medicine· 0 citations· 40 references
Medicine
TL;DR
These emerging developments and methodological trends are surveyed and future directions and challenges for safe, reliable thyroid imaging deployments are outlined, indicating a broader movement toward AI frameworks that operate as cohesive, clinically aligned components of the thyroid imaging ecosystem.
This review summarizes AI applications in thyroid ultrasound, including image preprocessing, nodule segmentation, quantitative feature analysis, benign-malignant differentiation, TIRADS optimization and automated reporting, and highlights AI’s potential in enhancing diagnostic consistency and accuracy.
Ye Guo, Tong Zhao, Li-Li Zhang et al.· Frontiers in Endocrinology· 1 citation
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for i...
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
Accurate preoperative risk stratification of cystic renal lesions (CRLs) remains a clinical challenge, primarily due to morphological overlap which often leads to the overtreatment of indolent cysts. To address this, we developed and validated an integrated deep learning framework that combines interactive segmenta...
Wen-Long Yuan, Ping-Lin He, Quan-Hao He et al.· Frontiers in Oncology· 0 citations
It is concluded that AI literacy is no longer optional: the radiologist's role is not threatened by AI but transformed by it—from film reader to AI-augmented clinical imaging physician capable of extending world-class diagnostic care to underserved populations across India and globally.
Emily Das, Rasel Mondal, Ashim Dhor et al.· Indian Journal of Radiology...· 0 citations
Background Accurate preoperative assessment of pulmonary nodule invasiveness remains challenging. We developed an internally validated multimodal framework integrating CT representations from a frozen vision foundation model with clinical variables. Methods This retrospective single-centre study included 1,179 patholog...
Jun-Bao Zhang, Yan Hou, Yi-Kang Yang et al.· Frontiers in Artificial Inte...· 0 citations
An explainable machine learning framework that integrates clinical and imaging data for early detection of brain tumors is proposed and supports the feasibility of trustworthy AI-assisted diagnostics in neuro-oncology, particularly for resource-constrained settings.
Mehran Ali, Zia Ullah, M. Ullah et al.· Journal of Engineering and C...· 0 citations
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