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Review
. 2025 Mar 11;15(6):689.
doi: 10.3390/diagnostics15060689.

AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review

Aanuoluwapo Clement David-Olawade  1 , David B Olawade  2   3   4 , Laura Vanderbloemen  5   6 , Oluwayomi B Rotifa  7 , Sandra Chinaza Fidelis  8 , Eghosasere Egbon  9 , Akwaowo Owoidighe Akpan  10 , Sola Adeleke  11   12 , Aruni Ghose  13   14 , Stergios Boussios  3   12   13   15   16   17
Affiliations
Review

AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review

Aanuoluwapo Clement David-Olawade et al. Diagnostics (Basel). .

Abstract

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic imaging and minimizing radiation exposure remains a fundamental challenge in radiology. Artificial intelligence (AI) has emerged as a transformative solution, enabling low-dose imaging protocols that enhance image quality while significantly reducing radiation doses. This review explores the role of AI-assisted low-dose imaging, particularly in CT, X-ray, and magnetic resonance imaging (MRI), highlighting advancements in deep learning models, convolutional neural networks (CNNs), and other AI-based approaches. These technologies have demonstrated substantial improvements in noise reduction, artifact removal, and real-time optimization of imaging parameters, thereby enhancing diagnostic accuracy while mitigating radiation risks. Additionally, AI has contributed to improved radiology workflow efficiency and cost reduction by minimizing the need for repeat scans. The review also discusses emerging directions in AI-driven medical imaging, including hybrid AI systems that integrate post-processing with real-time data acquisition, personalized imaging protocols tailored to patient characteristics, and the expansion of AI applications to fluoroscopy and positron emission tomography (PET). However, challenges such as model generalizability, regulatory constraints, ethical considerations, and computational requirements must be addressed to facilitate broader clinical adoption. AI-driven low-dose imaging has the potential to revolutionize radiology by enhancing patient safety, optimizing imaging quality, and improving healthcare efficiency, paving the way for a more advanced and sustainable future in medical imaging.

Keywords: CT scans; artificial intelligence; deep learning; low-dose imaging; radiation safety; radiology.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Comparison of radiation exposure, image quality, AI processing workflow, and diagnostic accuracy in standard vs. AI-assisted low-dose CT (LDCT) scanning.
Figure 2
Figure 2
PRISMA flow diagram of the article selection process.
Figure 3
Figure 3
Distribution of selected papers across various themes.
Figure 4
Figure 4
Simplified AI workflow in radiology: from medical image input to AI-assisted diagnosis. The figure illustrates the key stages where AI contributes to radiology, starting from image input, followed by AI processing through classification, segmentation, and image enhancement, leading to AI-assisted diagnosis with improved detection of abnormalities.
Figure 5
Figure 5
Simplified workflow of AI applications in medical imaging.
See this image and copyright information in PMC

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