Fairness of artificial intelligence in healthcare: review and recommendations
In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …
Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction
Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have
shown great potential in enhancing diagnosis and prognosis prediction in patients with …
shown great potential in enhancing diagnosis and prognosis prediction in patients with …
Clinical impact of deep learning reconstruction in MRI
Deep learning has been recognized as a paradigm-shifting tool in radiology. Deep learning
reconstruction (DLR) has recently emerged as a technology used in the image …
reconstruction (DLR) has recently emerged as a technology used in the image …
Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports
T Nakaura, N Yoshida, N Kobayashi, K Shiraishi… - Japanese Journal of …, 2024 - Springer
Purpose In this preliminary study, we aimed to evaluate the potential of the generative pre-
trained transformer (GPT) series for generating radiology reports from concise imaging …
trained transformer (GPT) series for generating radiology reports from concise imaging …
Clinical applications of artificial intelligence in liver imaging
This review outlines the current status and challenges of the clinical applications of artificial
intelligence in liver imaging using computed tomography or magnetic resonance imaging …
intelligence in liver imaging using computed tomography or magnetic resonance imaging …
New trend in artificial intelligence-based assistive technology for thoracic imaging
Although there is no solid agreement for artificial intelligence (AI), it refers to a computer
system with intelligence similar to that of humans. Deep learning appeared in 2006, and …
system with intelligence similar to that of humans. Deep learning appeared in 2006, and …
The impact of large language models on radiology: a guide for radiologists on the latest innovations in AI
Abstract The advent of Deep Learning (DL) has significantly propelled the field of diagnostic
radiology forward by enhancing image analysis and interpretation. The introduction of the …
radiology forward by enhancing image analysis and interpretation. The introduction of the …
Current state of artificial intelligence in clinical applications for head and neck MR imaging
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the
widespread application of head and neck MRI in clinical practice serves to assess various …
widespread application of head and neck MRI in clinical practice serves to assess various …
Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and …
M Ishihara, M Shiiba, H Maruno, M Kato… - Japanese Journal of …, 2023 - Springer
Purpose We evaluated the diagnostic performance of a clinically available deep learning-
based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) …
based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) …
Exploring the impact of super-resolution deep learning on MR angiography image quality
M Hokamura, H Uetani, T Nakaura, K Matsuo, K Morita… - Neuroradiology, 2024 - Springer
Purpose The aim of this study is to assess the effect of super-resolution deep learning-based
reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial …
reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial …