AI can speed up precise detection of one of the key signs of Alzheimer’s disease, according to researchers from University of California Davis and UC San Francisco, who published a study on their machine learning tool in Nature Communications.
The simultaneous advances of deep learning and radiomics may soon yield a single unified framework for clinical decision support that has the potential to “completely revolutionize the field of precision medicine.”
The FDA has given 510(k) clearance to an AI alert for urgent finding of a collapsed lung in chest X-rays. The approval is a first for an AI-based chest X-ray solution that can help doctors make quicker diagnoses from one of the world’s most used imaging modalities.
A machine-learning algorithm has surpassed four commonly used methods for catching sepsis early in hospital patients, giving clinicians up to 48 hours to intervene before the condition has a chance to begin turning dangerous.
A deep-learning algorithm trained entirely on open-source images has outperformed 136 of 157 dermatologists at classifying melanoma, according to a study running in the May edition of the European Journal of Cancer.
Researchers from the University of California, Los Angeles, have developed an artificial neural network capable of identifying and diagnosing prostate cancer almost as well as radiologists with a decade of experience.
A deep learning model trained on more than 1.5 million electrocardiograms and developed by a team at the Mayo Clinic improved detection rates for hyperkalemia in patients with chronic kidney disease (CKD), according to a study published April 3 in JAMA Cardiology.