Machine learning was able not only to detect speech patterns indicative of psychosis, but also to identify a new pattern associated with the prodromal phase of psychosis, enabling an algorithm to predict the later emergence of psychosis with more than 90 percent accuracy, according to a study published June 13 in npj Schizophrenia.

Indianapolis-based Community Health Network has tapped M*Modal's conversational artificial intelligence platform to streamline clinical workflows using virtual scribing and automated transcription technology.

Almost exactly one year after issuing its first policy recommendations on the use of artificial intelligence in medicine, the American Medical Association clarified its goals regarding medical AI and expanded its corresponding list of policy recommendations during the AMA House of Delegates annual meeting this week.

State-of-the-art machine learning algorithms diagnose benign and malignant skin lesions more accurately than dermatologists, dermatology residents and general practitioners, according to a study published June 11 in The Lancet Oncology.

IBM released its inaugural list of 40 women around the world who are pioneering the use of artificial intelligence in their industries, including two from the healthcare sector.

An artificial intelligence tool that alerts radiologists to critical cervical spine fractures to ensure timely diagnoses received FDA clearance, Israeli company Aidoc announced June 11.

University of Virginia nephrologist and director of dialysis system Brendan Bowman, MD, has teamed up with engineers at the university to develop predictive techniques to improve medication dosing protocols.

Patient notes are critical to quality patient care, but for physicians, medical documentation may at times feel at odds with this goal.

New York City-based Mount Sinai Health System's Icahn School of Medicine announced on June 11 the launch of the Hamilton and Amabel James Center for Artificial Intelligence and Human Health, which will promote the use of AI, data science and genomics to further disease research and treatment.

A machine learning model produced low-dose CT images with greater speed and accuracy than previous attempts to use less radiation in CT imaging, according to a study published this week in Nature Machine Intelligence.

We may still be many years away from seeing automation integrated across entire healthcare organizations, but many have already begun to incorporate artificial intelligence into clinical workflows on a case-by-case basis.

The artificial intelligence-powered Sugar.IQ diabetes assistant app can help patients stay within optimal glycemic range for longer periods of time and significantly reduce hypoglycemic events, according to study data presented by IBM Watson Health and Medtronic at the American Diabetes Association's Scientific Sessions on June 10.

Software that can detect and classify facial dysmorphism from front-facing portraits of patients can further improve the abilities of artificial intelligence systems to diagnose rare genetic diseases, according to a study published June 5 in Genetics in Medicine.

Louisville, Ky., Mayor Greg Fischer announced June 7 that the city has formed an "AI Innovation Digital Alliance" with Microsoft to provide support to the local healthcare and manufacturing industries as they implement artificial intelligence and other advanced technologies.

When assisted by a new deep learning model, radiologists saw a marked improvement in how accurately they detected brain aneurysms from CT angiography imaging, according to a study published in JAMA Network Open this month.

The American Medical Informatics Association submitted a letter to the FDA this week not only praising the agency's proposed regulatory framework concerning the use of self-updating artificial intelligence in medicine, but also encouraging even stricter regulations.

Natural language processing can analyze babies' "cry signals" to distinguish their meanings in home and healthcare settings, according to a study published in the latest issue of the IEEE/CAA Journal of Automatica Sinica.

A machine learning-powered mobile system can distinguish between and diagnose common respiratory disorders including asthma and pneumonia by analyzing the sound of a child's cough, according to a study published June 6 in Respiratory Research.

Baltimore-based University of Maryland Medical Systems has deployed an in-house machine learning model, called the Baltimore score, that aims to better predict which discharged patients are likely to be readmitted.

Computer scientists have secured funding to develop artificial intelligence that can automatically identify signs of early-stage oral cancer using an existing screening app.