T. Martín-Noguerol

Agentic AI in Radiology: Fundamentals, Drivers, and Gaps.

RATIONALE AND OBJECTIVES Agentic artificial intelligence (AI) represents an emerging paradigm aimed at overcoming the limitations of current narrow, task-specific AI applications in radiology by enabling coordinated systems that integrate multiple tools, data sources, and workflows. Unlike isolated algorithms, Agentic...

Advanced neuroimaging in rare diseases: The paradigm of quantification in the era of personalized medicine.

Rare diseases frequently involve the central nervous system, posing significant diagnostic and monitoring challenges. Recent advances in Magnetic Resonance Imaging (MRI) and neuroradiology have expanded the tools available for RD assessment, encompassing improvements in image acquisition, postprocessing techniques, and...

Photon-counting CT in head and neck radiology: bridging detector physics and clinical applications.

Automatic Patient Eligibility for Photon-counting CT Using Discriminative and Generative AI Models in Neuroradiology.

Discriminative transformer models, particularly RoBERTa, enabled highly accurate automatic identification of CT requests requiring AP to be performed at PCCT, substantially outperforming general-purpose generative LLMs, and support this exploratory proof-of-concept study of AI-driven eligibility routing to optimize PCC...

Optimizing gadolinium-enhanced CNS MRI: From physics to clinical practice.

A comprehensive, technically optimized, and physiologically informed approach to GBCA use is crucial to maximizing diagnostic yield while minimizing potential risks in CNS MRI.