The current and future state of AI interpretation of medical images
P Rajpurkar, MP Lungren - New England Journal of Medicine, 2023 - Mass Medical Soc
The Current and Future State of AI Interpretation of Medical Images | New England Journal of
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AI in health and medicine
Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
experiences of both clinicians and patients. We discuss key findings from a 2-year weekly …
Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …
medical image analysis, potentially improving healthcare and patient outcomes. However …
[HTML][HTML] Role of artificial intelligence applications in real-life clinical practice: systematic review
Background Artificial intelligence (AI) applications are growing at an unprecedented pace in
health care, including disease diagnosis, triage or screening, risk analysis, surgical …
health care, including disease diagnosis, triage or screening, risk analysis, surgical …
Benchmarking saliency methods for chest X-ray interpretation
Saliency methods, which produce heat maps that highlight the areas of the medical image
that influence model prediction, are often presented to clinicians as an aid in diagnostic …
that influence model prediction, are often presented to clinicians as an aid in diagnostic …
BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
A brain tumor is a mass that grows unevenly in the brain and directly affects human life. This
mass occurs spontaneously because of the tissues surrounding the brain or the skull …
mass occurs spontaneously because of the tissues surrounding the brain or the skull …
Combining human expertise with artificial intelligence: Experimental evidence from radiology
ABSTRACT While Artificial Intelligence (AI) algorithms have achieved performance levels
comparable to human experts on various predictive tasks, human experts can still access …
comparable to human experts on various predictive tasks, human experts can still access …
A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images
Z Shi, C Miao, UJ Schoepf, RH Savage… - Nature …, 2020 - nature.com
Intracranial aneurysm is a common life-threatening disease. Computed tomography
angiography is recommended as the standard diagnosis tool; yet, interpretation can be time …
angiography is recommended as the standard diagnosis tool; yet, interpretation can be time …
Advances in deep learning-based medical image analysis
Importance. With the booming growth of artificial intelligence (AI), especially the recent
advancements of deep learning, utilizing advanced deep learning-based methods for …
advancements of deep learning, utilizing advanced deep learning-based methods for …
Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI
Background Assessment of lumbar spinal stenosis at MRI is repetitive and time consuming.
Deep learning (DL) could improve productivity and the consistency of reporting. Purpose To …
Deep learning (DL) could improve productivity and the consistency of reporting. Purpose To …