Machine Learning

Common sight-threatening retinal diseases seen by all comprehensive ophthalmologists include wet age-related macular degeneration, geographic atrophy and diabetic macular edema.

Retinal diseases are a major cause of severe vision loss, with numbers constantly increasing due to the aging of the population, the diabetes epidemic and the growing prevalence of high myopia.

Researchers at University of Michigan Health developed an artificial intelligence model to accurately assess risk for post-procedural outcomes after PCI.

Machine learning algorithms can be effective in predicting osteoporosis among patients with rheumatoid arthritis, as well as identifying new predictive factors, according to data published in Nature Scientific Reports.

ANAHEIM, Calif. — An artificial intelligence model counted and classified grains of pollen almost as accurately as a human being, according to a poster at the American College of Allergy, Asthma & Immunology Annual Scientific Meeting.

An artificial intelligence-based machine learning model that diagnosed cognitive impairment in Alzheimer’s disease extracted information more accurately than standard screening or human analysis, according to a poster presentation at CTAD.

Last year, I partnered with colleagues at the National Institute of Standards and Technology, or NIST, to examine the challenges represented by artificial intelligence bias and identify some preliminary guidance for addressing them.

With nearly 90% accuracy, an artificial intelligence-based model consistently predicted atrial fibrillation within 31 days among patients with sinus rhythm, according to findings published in JAMA Cardiology.

InfoBionic announced it received FDA 510(k) clearance for its next-generation remote ECG monitoring device with a Bluetooth diagnostic six-lead sensor for patients who require cardiac monitoring.

For children in critical condition who struggle to breathe, ventilators are essential machines; however, it is important to recognize that they may unintentionally hurt the patient.