The Role of Artificial Intelligence in Medical Imaging and Diagnostics – A Radiographer’s Perspective

DOI

10.2991/978-94-6239-823-8_18How to use a DOI?

Keywords

Modern radiology; AI-based algorithms; Image reconstruction

Abstract

Artificial intelligence (AI) has become an integral part of healthcare, particularly in medical imaging. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are essential diagnostic modalities with different technical characteristics and clinical applications. The integration of AI into these imaging techniques supports radiographers by improving workflow efficiency, image quality, and patient safety. In MRI, deep learning-based image reconstruction enables faster image acquisition while enhancing signal-to-noise ratio and spatial resolution, reducing examination times without compromising diagnostic quality. AI-driven tools, including automated anatomical region selection, slice positioning, and protocol optimization, contribute to standardized and reproducible examinations while reducing operator-dependent variability. In CT, AI-based iterative reconstruction algorithms reduce image noise and improve image quality, particularly in low-dose imaging. Automatic dose optimization systems further enhance patient safety by adapting scanning parameters to patient size and the examined anatomical region. In addition, AI-assisted post-processing and structured reporting support radiologists by improving workflow efficiency and facilitating clinical decision-making. Overall, the combination of advanced imaging technology and AI-based software solutions has significantly improved the efficiency, quality, and safety of MRI and CT examinations. These developments optimize clinical workflows, support radiographers and radiologists in daily practice, and ultimately contribute to improved patient care.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Anett Szalkó
AU  - Balázs Gadóczi
AU  - Andrea Lakatos
AU  - Attila Károly Varga
PY  - 2026
DA  - 2026/11/04
TI  - The Role of Artificial Intelligence in Medical Imaging and Diagnostics – A Radiographer’s Perspective
BT  - Proceedings of the Innovation in Engineering Education 2026 Conference (IEE 2026)
PB  - Atlantis Press
SP  - 209
EP  - 217
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6239-823-8_18
DO  - 10.2991/978-94-6239-823-8_18
ID  - Szalkó2026
ER  -