Machine learning in medicine
Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …
interactions are informed and supported by massive amounts of data from interactions with …
High-performance medicine: the convergence of human and artificial intelligence
EJ Topol - Nature medicine, 2019 - nature.com
The use of artificial intelligence, and the deep-learning subtype in particular, has been
enabled by the use of labeled big data, along with markedly enhanced computing power …
enabled by the use of labeled big data, along with markedly enhanced computing power …
Key challenges for delivering clinical impact with artificial intelligence
Background Artificial intelligence (AI) research in healthcare is accelerating rapidly, with
potential applications being demonstrated across various domains of medicine. However …
potential applications being demonstrated across various domains of medicine. However …
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic
factors, potentially determining treatment independent of the stage. However, Gleason …
factors, potentially determining treatment independent of the stage. However, Gleason …
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets
Owing to improvements in image recognition via deep learning, machine-learning
algorithms could eventually be applied to automated medical diagnoses that can guide …
algorithms could eventually be applied to automated medical diagnoses that can guide …
Intracerebral hemorrhage: an update on diagnosis and treatment
IC Hostettler, DJ Seiffge, DJ Werring - Expert review of …, 2019 - Taylor & Francis
Introduction: Spontaneous non-traumatic intracerebral hemorrhage (ICH) is most often
caused by small vessel diseases: deep perforator arteriopathy (hypertensive arteriopathy) or …
caused by small vessel diseases: deep perforator arteriopathy (hypertensive arteriopathy) or …
Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning
Computed tomography (CT) of the head is used worldwide to diagnose neurologic
emergencies. However, expertise is required to interpret these scans, and even highly …
emergencies. However, expertise is required to interpret these scans, and even highly …
Recent advances of deep learning in bioinformatics and computational biology
Extracting inherent valuable knowledge from omics big data remains as a daunting problem
in bioinformatics and computational biology. Deep learning, as an emerging branch from …
in bioinformatics and computational biology. Deep learning, as an emerging branch from …
Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model
Importance Deep learning has the potential to augment clinician performance in medical
imaging interpretation and reduce time to diagnosis through automated segmentation. Few …
imaging interpretation and reduce time to diagnosis through automated segmentation. Few …
Deep learning and neurology: a systematic review
AAA Valliani, D Ranti, EK Oermann - Neurology and therapy, 2019 - Springer
Deciphering the massive volume of complex electronic data that has been compiled by
hospital systems over the past decades has the potential to revolutionize modern medicine …
hospital systems over the past decades has the potential to revolutionize modern medicine …