Using a computer algorithm, scientists at Uppsala University have identified a promising new treatment for neuroblastoma.
Artificial intelligence has unimaginable potential to revolutionize medicine. We cover the latest technology breakthroughs of machine learning and deep learning algorithms that process mindboggling amounts of data, spot even the smallest detail in medical images and help medical professionals in designing treatment plans.
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.
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.
A new statistical technique from the field of machine learning is now making it possible to predict the success of smartphone-based interventions more accurately.
Researchers have discovered that a population of neurons in the brain’s frontal lobe contain stable short-term memory information within dynamically-changing neural activity.
Using machine learning, a prototype microscope teaches itself the best illumination settings for diagnosing malaria.
Ensembles created using models submitted to the RSNA Pediatric Bone Age Machine Learning Challenge convincingly outperformed single-model prediction of bone age.
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.
Researchers have shown that AI can evaluate written messages by patients with severely diseased livers to detect language abnormalities associated with liver disease.
Researchers have been investigating whether artificial intelligence might be used to steer a catheter automatically and reliably to a blocked blood vessel.
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 Thomas Jefferson University use machine learning on ultrasound images of thyroid nodules to predict risk of malignancy.
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.
An AI tool identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists.