AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review DOI Creative Commons
Aanuoluwapo Clement David-Olawade, David B. Olawade, Laura Vanderbloemen

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 689 - 689

Published: March 11, 2025

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

Language: Английский

Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap DOI
Irene Dankwa‐Mullan, Kingsley Ndoh, Darlington Ahiale Akogo

et al.

Current Oncology Reports, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0
AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review DOI Creative Commons
Aanuoluwapo Clement David-Olawade, David B. Olawade, Laura Vanderbloemen

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 689 - 689

Published: March 11, 2025

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

Language: Английский

Citations

0