A portable surveillance device powered by machine learning can detect coughing and crowd size in real time, then analyze the data to directly monitor flu-like illnesses trends.
By adding infrared capability to the ubiquitous, standard optical microscope, researchers hope to bring cancer diagnosis into the digital era.
A novel method of combining advanced optical imaging with an artificial intelligence algorithm produces accurate, real-time intraoperative diagnosis of brain tumors.
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.
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.
Dementia screening could be as easy as using a smartphone app that listens to elderly people speak.
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 AI tool identified breast cancer with approximately 90 percent accuracy when combined with analysis by radiologists.
Based on a convolutional neural network the tool is able to provide results within seconds, thus supporting the doctor with comprehensive image analysis.