Diagnostics

Stanford researchers have demonstrated a way to remotely diagnose autism in children in Bangladesh.

A deep-learning algorithm trained entirely on open-source images has outperformed 136 of 157 dermatologists at classifying melanoma, according to a study running in the May edition of the European Journal of Cancer.

Researchers from the University of California, Los Angeles, have developed an artificial neural network capable of identifying and diagnosing prostate cancer almost as well as radiologists with a decade of experience.

A rare and difficult-to-diagnose genetic condition that raises LDL (bad) cholesterol to dangerous levels is now vulnerable to an AI tool.

A deep learning model trained on more than 1.5 million electrocardiograms and developed by a team at the Mayo Clinic improved detection rates for hyperkalemia in patients with chronic kidney disease (CKD), according to a study published April 3 in JAMA Cardiology.

Mental health researchers at Harvard and the University of Pennsylvania have developed a novel machine learning technique for predicting how bipolar patients will respond to two drugs commonly prescribed to treat the disorder, according to a study running in Bipolar Disorders.

Artificial intelligence is more effective at detecting cervical cancer than established lab tests, according to a pilot study out of Seoul, South Korea.

The promise of artificial intelligence to revolutionize healthcare is the topic of increasing research, with new publications every day devoted to the topic. One of these applications, according to an April 1 article in The Wall Street Journal, is using AI to listen to a person’s voice and detect a range of mental and physical ailments, including coronary artery disease (CAD).

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.