More than 3.4 million people in the US and 65 million people worldwide have epilepsy, a neurological disorder that affects the nervous system and causes seizures. One in 26 people will develop epilepsy at some point in their lives, and 1 out of 1000 people with epilepsy die from unexpected deaths each year.

Like many conditions, epilepsy treatment starts with early detection.

Using artificial intelligence (AI), breast radiologists in Denmark have improved breast cancer screening performance and reduced the rate of false-positive findings. Results of the study were published today in Radiology, a journal of the Radiological Society of North America (RSNA).

Mammography successfully reduces breast cancer mortality, but also carries the risk of false-positive findings. In recent years, researchers have studied the use of AI systems in screening.

Researchers who had been using Fitbit data to help predict surgical outcomes have a new method to more accurately gauge how patients may recover from spine surgery.

Using machine learning techniques developed at the AI for Health Institute at Washington University in St. Louis, Chenyang Lu, the Fullgraf Professor in the university's McKelvey School of Engineering, collaborated with Jacob Greenberg, MD, assistant professor of neurosurgery at the School of Medicine, to develop a way to predict recovery more accurately from lumbar spine surgery.

The use of artificial intelligence (AI) in clinical health care has the potential to transform health care delivery but it should not replace physician decision-making, says the American College of Physicians (ACP) in a new policy paper. "Artificial Intelligence in the Provision of Health Care," published in the Annals of Internal Medicine, offers recommendations on the ethical, scientific, and clinical components of AI use, and says that AI tools and systems should enhance human intelligence, not supplant it.

A combination of facial thermal imaging and artificial intelligence (AI) can accurately predict the presence of coronary artery disease, finds research published in the open access journal BMJ Health & Care Informatics.

This non-invasive real-time approach is more effective than conventional methods and could be adopted for clinical practice to improve the accuracy of diagnosis and workflow, pending testing on larger and more ethnically diverse numbers of patients, suggest the researchers.

A new study in JMIR Cardio, published by JMIR Publications, shows that a fully digital, artificial intelligence (AI)-driven lifestyle coaching program can effectively reduce blood pressure (BP) in adults with hypertension. This AI-based program leverages data from wearable activity trackers and BP monitors as well as a mobile app questionnaire to tailor lifestyle guidance.

Research led by the University of Plymouth has shown that a new deep learning AI model can identify what happens and when during embryonic development, from video.

Published in the Journal of Experimental Biology, the study highlights how the model, known as Dev-ResNet, can identify the occurrence of key functional developmental events in pond snails, including heart function, crawling, hatching and even death.