Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Data drift in medical machine learning: implications and potential remedies
Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …
model and that applied to the model in real-world operation. Medical ML systems can be …
[HTML][HTML] Deep learning-aided decision support for diagnosis of skin disease across skin tones
Although advances in deep learning systems for image-based medical diagnosis
demonstrate their potential to augment clinical decision-making, the effectiveness of …
demonstrate their potential to augment clinical decision-making, the effectiveness of …
Explainable artificial intelligence and cardiac imaging: toward more interpretable models
Artificial intelligence applications have shown success in different medical and health care
domains, and cardiac imaging is no exception. However, some machine learning models …
domains, and cardiac imaging is no exception. However, some machine learning models …
Trustworthy multi-phase liver tumor segmentation via evidence-based uncertainty
Multi-phase liver contrast-enhanced computed tomography (CECT) images convey the
complementary multi-phase information for liver tumor segmentation (LiTS), which are …
complementary multi-phase information for liver tumor segmentation (LiTS), which are …
A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations
Purpose There is a growing body of diagnostic performance studies for emergency
radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is …
radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is …
[HTML][HTML] Mitigating knowledge imbalance in AI-advised decision-making through collaborative user involvement
Integrating artificial intelligence (AI) systems into decision-making tasks attempts to assist
people by augmenting or complementing their abilities and ultimately improve task …
people by augmenting or complementing their abilities and ultimately improve task …
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
Abstract Background AI/ML CAD tools can potentially improve outcomes in the high-stakes,
high-volume model of trauma radiology. No prior scoping review has been undertaken to …
high-volume model of trauma radiology. No prior scoping review has been undertaken to …
[HTML][HTML] The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation
SL McNamara, PH Yi, W Lotter - NPJ Digital Medicine, 2024 - nature.com
As applications of AI in medicine continue to expand, there is an increasing focus on
integration into clinical practice. An underappreciated aspect of this clinical translation is …
integration into clinical practice. An underappreciated aspect of this clinical translation is …
[HTML][HTML] The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI
M Santorsola, F Lescai - New Biotechnology, 2023 - Elsevier
Deep learning has already revolutionised the way a wide range of data is processed in
many areas of daily life. The ability to learn abstractions and relationships from …
many areas of daily life. The ability to learn abstractions and relationships from …