Currently, we are too focused on the topic of AI. In order, however, to leverage AI technology several challenges have to be mastered and a proper framework has to be established.
Usind deep learning and digital scanning of conventional hematoxylin and eosin-stained tumor tissue sections, researchers have developed a clinically useful prognostic marker.
A deep learning model can identify sleep stages as accurately as an experienced physician.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
A deep neural network model helps predict healthcare visits by elderly people, with the potential to save millions.
An AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.
Radiologists assisted by deep learning based software were better able to detect malignant lung cancers on chest X-rays.
Researchers show that deep learning algorithms perform similar to human experts when classifying blood samples from patients suffering from acute myeloid leukemia.
An algorithm did better than experts radiologists at finding tiny brain hemorrhages in head scans — an advance that one day may help doctors treat patients with strokes.
Combining new wearable electronics and a deep learning algorithm could help disabled people wirelessly interact with a computer.
A research team has succeeded in identifying specific patterns in Electro-Encephalogram (EEG) analyses that the deep learning network uses for making prognosis decisions.
Patients could soon get faster and more accurate diagnoses with new software that can automatically detect signs of diabetes, heart disease and cancer from medical images.
Researchers have developed a groundbreaking AI algorithm that can enable hearing aid users to take a more active part in conversations in noisy environments.