Scientists and collaborators are using machine learning to address two key barriers to industrialization of two-photon lithography.
A deep learning powered single-strained electronic skin sensor can capture human motion from a distance.
Pathologists who examined the computationally stained images could not tell them apart from traditionally stained slides.
Researchers have developed a new model that accurately and automatically shows the exact location of mandibular canals.
“AI is the biggest technological breakthrough of our lifetime. It will boost the entire healthcare ecosystem and will eventually re-invent the way we deliver medicine entirely.”
An AI algorithm is capable of diagnosing 134 skin disorders and supporting specialists by augmenting the accuracy of diagnoses and predicting treatment options.
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.