AI APPLICATIONS IN CT AND MRI IMAGING: CURRENT ADVANCES AND LIMITATIONS. A NARRATIVE REVIEW

Aleksandra Cyrkler; Urszula Marzec; Karolina  Wymoczył; Natalia Cieślak; Agata  Chodkowska; Karol Dąbek; Kamila  Czyżak; Eliza Rajca; Agnieszka Giza; Paulina Giza

doi:10.31435/ijitss.2(50).2026.5103

Authors

Aleksandra Cyrkler Stefan Cardinal Wyszyński Provincial Specialist Hospital SPZOZ in Lublin, Aleja Kraśnicka 100, 20-718 Lublin, Poland https://orcid.org/0009-0001-6865-7450
Urszula Marzec Stefan Cardinal Wyszyński Provincial Specialist Hospital SPZOZ in Lublin, Aleja Kraśnicka 100, 20-718 Lublin, Poland https://orcid.org/0009-0000-6348-184X
Karolina Wymoczył Stefan Cardinal Wyszyński Provincial Specialist Hospital SPZOZ in Lublin, Aleja Kraśnicka 100, 20-718 Lublin, Poland https://orcid.org/0009-0004-7326-8013
Natalia Cieślak Medical University of Lublin, Lublin, Poland https://orcid.org/0009-0004-6355-8164
Agata Chodkowska Medical University of Lublin, Lublin, Poland https://orcid.org/0009-0000-5006-3040
Karol Dąbek John Paul II Municipal Hospital in Rzeszów, 4 Rycerska St., 35-241 Rzeszów, Poland https://orcid.org/0009-0005-6152-3870
Kamila Czyżak John Paul II Municipal Hospital in Rzeszów, 4 Rycerska St., 35-241 Rzeszów, Poland https://orcid.org/0000-0002-8866-5810
Eliza Rajca Provincial Integrated Hospital in Kielce, Kielce, Poland https://orcid.org/0009-0005-6024-4298
Agnieszka Giza Independent Public Clinical Hospital No. 4 in Lublin, ul. Dra K. Jaczewskiego 8, 20-090 Lublin, Poland https://orcid.org/0009-0002-5741-3482
Paulina Giza Medical University of Warsaw, Warsaw, Poland https://orcid.org/0009-0004-7637-5906

DOI:

https://doi.org/10.31435/ijitss.2(50).2026.5103

Keywords:

Artificial Intelligence; Deep Learning; Medical Imaging; Computed Tomography; Magnetic Resonance Imaging; Image Reconstruction; Diagnostic Correctness; Multimodal Data Integration; Radiology Workflow; Clinical Decision Support

Abstract

Artificial intelligence (AI), primarily deep learning, is transforming medical imaging. Deep learning processes intricate data using layered neural networks. These techniques are also particularly applicable in computed tomography (CT), which utilizes X-rays to generate images, and magnetic resonance imaging (MRI), which utilizes magnetic fields and radio waves to generate detailed images. This review reviews advances made recently in AI detection, segmentation, classification, reconstruction, and the fusion of different data modalities in the CT and MRI fields. It also examines their impact in actual clinical practice. Studies show that deep learning models perform well in detecting lung nodules, brain tumors, and imaging of cardiovascular and abdominal organs. Besides this, AI methods based on reconstruction can shorten CT radiation doses and speed up MRI. And the best image quality is preserved.

Despite these results, obstacles remain that stop AI from being used for medical imaging. Most difficult problems encountered include limited testing on external data of different types at different institutions, the performance differences across different kinds of datasets, also deep learning models make decisions quite obscurely. Other persisting problems are how to make models more readable, how they can be integrated within clinical workflows, and regulations. The need to build more artificial intelligence that integrates imaging with clinical, genetic, and pathological data is an essential issue for future studies. Such methods could help predict better, personalize medicine. AI alone has potentially transformed radiology: from finding new ways to make diagnoses more efficient, standardizing interpretation of images and supporting clinical decision-making.

Nevertheless, AI ought to be viewed as a tool to aid radiologists — not a replacement. Such studies should be focused on testing AI in actual clinical environments, increasing the transparency of algorithms, and establishing standardized measurement methods for performance. These stages are required to securely and effectively embed AI in routine CT and MRI operations.

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