Artificial intelligence (AI) is increasingly integrated into neuroradiology practice, with a growing number of FDA-cleared algorithms now supporting tasks ranging from acute triage to volumetric analysis. This review provides a structured overview of commercially available, FDA-regulated AI tools in neuroradiology, organized by clinical application. These include detection and prioritization of intracranial hemorrhage and large vessel occlusion, aneurysm identification on CTA, automated ASPECTS scoring, and brain tumor segmentation, as well as tools for image enhancement and quantitative analysis in neurodegenerative and demyelinating diseases. For each application, we describe the algorithm's intended function, summarize available performance data, and highlight areas where AI can add clinical value, such as reducing time to diagnosis, improving detection of subtle findings, or standardizing measurements. We also discuss key limitations, including reduced performance outside intended-use parameters and the need for broader validation. As AI tools continue to evolve, understanding their strengths, limitations, and optimal use cases is essential to their safe and effective deployment in neuroradiology.

Coronary artery disease (CAD) continues to be a leading cause of death globally. Radiological evaluation of CAD generally consists of stenosis detection by cardiac computed tomographic angiography (CCTA). However, this approach can be inefficient and subject to inter-observer variability. In this review we explore the newest developments related to CAD evaluation by CCTA, with an emphasis on plaque quantification. Artificial Intelligence-based quantitative computed tomography (AI-QCT) offers an efficient and reproducible method to analyse plaque biomarkers that support more refined risk-stratification for major adverse cardiovascular events (MACE). Artificial intelligence-based coronary stenosis quantification (AI-CSQ) can streamline workflow and improve consistency. The utilisation of a photon counting detector CT (PCD-CT) has been demonstrated to enhance spatial resolution, thereby allowing more precise coronary lumen analysis and artifact reduction. Fractional flow reserve-computed tomography (FFR-CT) delivers a non-invasive physiologic assessment of stenotic lesions. Finally, we discuss the impact of these changes on risk stratification and guiding preventive therapy as well as possible future directions in this rapidly evolving field.

Integrated positron emission tomography/magnetic resonance imaging (PET/MR) combines the molecular sensitivity of PET with the superior soft-tissue contrast and multiparametric capabilities of MRI, enabling simultaneous acquisition and improved spatial-temporal alignment compared with PET/CT. Since its introduction, PET/MR has evolved through major detector and system-level innovations, particularly the adoption of avalanche photodiodes and silicon photomultipliers, enabling fully integrated clinical systems. PET/MR presents unique technical challenges and opportunities. This review summarizes the historical development and current state of PET/MR systems and highlights key methodological advances, including attenuation correction, MRI-assisted motion correction, anatomically guided PET reconstruction, spatiotemporal denoising for dynamic imaging, and imaging-derived input function (IDIF) techniques. A brief discussion about clinical applications will be presented. Overall, PET/MR represents a powerful, evolving hybrid imaging platform with significant potential to advance precision medicine and quantitative imaging.