Usind deep learning and digital scanning of conventional hematoxylin and eosin-stained tumor tissue sections, researchers have developed a clinically useful prognostic marker.
Using a computer algorithm, scientists at Uppsala University have identified a promising new treatment for neuroblastoma.
An AI has successfully found features in pathology images from human cancer patients, without annotation, that could be understood by human doctors.
A software tool uses artificial intelligence to recognize cancer cells from digital pathology images — giving clinicians a powerful way of predicting patient outcomes.
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.
Using machine learning, researchers have built a tool that detects genetic mutations that trigger the immune system, helping identify which cancer patients are likely to benefit from immunotherapy.
Researchers from Johns Hopkins Medicine show that wearable activity trackers are a reliable tool for predicting death risk in older adults.
Researchers from Thomas Jefferson University use machine learning on ultrasound images of thyroid nodules to predict risk of malignancy.
Researchers used artificial intelligence to develop a new classification method which identifies the primary origins of cancerous tissue based on chemical DNA changes.
Collaborators are developing an endoscopic robotic system with two-handed dexterity at a much smaller scale than existing options.
Scientists have developed a robot that looks and moves like a jellyfish; the aim is for Jellyfishbot to be applied in the treatment of cancer.
Researchers have utilized machine learning capabilities to assist with the challenging task of grading tumor patterns and subtypes of lung adenocarcinoma.
Scientists are using 3D technology to help rebuild the faces of cancer patients, those hurt in accidents and people born with complex facial deformities.