Artificial intelligence (AI) has become an integral part of everyday life in recent years, with particularly significant impact on healthcare and medical imaging diagnostics. Among imaging modalities, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) play a fundamental role in clinical practice. Due to their different physical principles and technical characteristics, these modalities offer distinct advantages and limitations too. The integration of AI-based algorithms into diagnostic imaging supports radiographers by enabling standardized, reproducible, and more efficient workflows, while simultaneously improving image quality and patient safety.

MRI is a multiplanar and multiparametric imaging modality with expanding diagnostic capabilities due to technological advancements. Consequently, not only examination protocols but also ongoing technological developments of MRI systems are of great importance. One of the main challenges of MRI is the relatively long examination time, which typically ranges from 20 to 60 minutes depending on the examined region and the applied protocol. A key innovation in this field is deep learning–based image reconstruction, which enables faster data acquisition while improving signal-to-noise ratio and spatial resolution. It’s offer new opportunities to reduce scan times without compromising diagnostic image quality, resulting in improved patient comfort. AI-driven tools such as automated anatomical region selection, automatic slice positioning, and protocol optimization significantly accelerate examination preparation and simplify daily clinical routine. These technologies reduce the occurrence of operator-related errors. Moreover, AI-Rad Companion is fully integrated into the image interpretation workflow, supporting radiologists through automated measurements and structured DICOM reporting. It enhances productivity by streamlining reading and reporting processes while maintaining full workflow control for evidence-based decision-making.

CT is one of the most widely used medical imaging modalities. It is a radiation-based technique that produces cross-sectional images of the body. One of the primary goals in CT imaging is radiation dose reduction while maintaining high diagnostic quality. Advanced AI-supported iterative reconstruction algorithms effectively reduce image noise and improve soft tissue contrast, which is particularly valuable in low-dose examinations. In addition, automatic dose optimization systems adjust scanning parameters based on patient size and the examined anatomical region, thereby enhancing patient safety and ensuring consistent image quality

In summary, the combination of advanced hardware developments and AI-based software solutions in both MRI and CT has led to faster, more efficient, and safer imaging examinations. Deep learning algorithms contribute to improved image quality, optimized workflows, and reduced examination times, ultimately supporting the work of radiographers and radiologists while improving overall patient care.