Diagnostics

A free web tool known as “Chester the AI Radiology Assistant” can assess a person’s chest X-rays online within seconds, ensuring patients’ private medical data remains secure while predicting their likelihood of having 14 diseases.

Machine learning approaches including deep learning and random forest greatly improved a University of Nottingham team’s ability to predict premature death in a study of half a million U.K. Biobank participants, according to research published in PLOS One.

A machine learning model developed by scientists at Google successfully documented and charted disease symptoms from patient-physician conversations in early tests, but the tech still has a long way to go, according to research published in JAMA Internal Medicine March 25.

A team of researchers in San Francisco have developed an EHR-driven deep learning model that’s able to accurately predict the prognosis of patients with rheumatoid arthritis (RA), according to a study published in JAMA Network Open.

A multitude of recent studies and success stories suggest artificial intelligence is on its way to topping doctors in accurately diagnosing diseases from asthma to breast cancer—seemingly a step in the right direction. But does the hype surrounding AI’s victories eclipse its shortcomings?

A deep neural network crafted by research specialists at Dartmouth’s Norris Cotton Cancer Center identified different types of lung adenocarcinoma as well as practicing pathologists in a recent study, according to work published March 4 in Scientific Reports.

Researchers from the Icahn School of Medicine at Mount Sinai have developed an AI platform that’s reportedly capable of detecting a range of neurodegenerative diseases in human brain tissue samples, according to a study published in the February issue of Laboratory Investigation.

Researchers at the Children’s Hospital of Philadelphia (CHOP) developed machine learning models that can detect the presence of sepsis in infants, hours before physicians. Findings from the study were published in PLOS One.

Though AI systems have shown promise for detecting skin cancer, more work is needed before they can be utilized in “real world” applications, according to researchers at the 2019 American Academy of Dermatology annual meeting in Washington, D.C.

OOVA, a Mount Sinai Health System spinout and diagnostic device company, is piloting an AI-based fertility tool that measures and monitors the concentrations of luteinizing hormone and progesterone—two key fertility hormones. The program is being piloted in collaboration with Thorne Research.

Researchers at the University of California, San Francisco found Google Translate, Google’s AI-based app, can help non-English speaking patients and their providers. However, it's not perfect, according to research published in JAMA Internal Medicine.

A new machine learning-based system, detailed in the journal Frontiers in Neurology, screens children for fetal alcohol spectrum disorder (FASD) in a quick and more cost-efficient way. The system was developed by researchers at the University of Southern California, Queen’s University in Ontario and Duke University, and will be accessible to children in more remote areas of the globe.