Researchers at UCL and University of Ghana have successfully predicted whether children have anaemia using only a set of smartphone images.

The study, published in PLOS ONE, brought together researchers and clinicians at UCL Engineering, UCLH and Korle Bu Teaching Hospital, Ghana to investigate a new non-invasive diagnostic technique using smartphone photographs of the eye and face.

A team of researchers from the University of British Columbia and BC Cancer have developed an artificial intelligence (AI) model that predicts cancer patient survival more accurately and with more readily available data than previous tools.

The model uses natural language processing (NLP) - a branch of AI that understands complex human language - to analyze oncologist notes following a patient’s initial consultation visit - the first step in the cancer journey after diagnosis.

Although investigators have made strides in detecting signs of Alzheimer's disease using high-quality brain imaging tests collected as part of research studies, a team at Massachusetts General Hospital (MGH) recently developed an accurate method for detection that relies on routinely collected clinical brain images. The advance could lead to more accurate diagnoses.

A "biocomputer" powered by human brain cells could be developed within our lifetime, according to Johns Hopkins University researchers who expect such technology to exponentially expand the capabilities of modern computing and create novel fields of study.

The team outlines their plan for "organoid intelligence" in the journal Frontiers in Science.

Northwestern University researchers have developed a first-of-its-kind small, flexible, stretchable bandage that accelerates healing by delivering electrotherapy directly to the wound site.

In an animal study, the new bandage healed diabetic ulcers 30% faster than in mice without the bandage.

Millions of people suffer from these painfully common conditions, and often endure inconvenient and invasive medical procedures to diagnose the causes.

In a study published in Nature Electronics, Khalil B. Ramadi, Assistant Professor of Bioengineering at NYU Tandon School of Engineering, revealed that he and a team of collaborators at MIT and Caltech have developed a tiny pill-like electromagnetic device that,

A new smartphone application called FAST.AI may help people who are having a stroke or their family and caregivers recognize common stroke symptoms in real time, prompting them to quickly call 9-1-1, according to preliminary research to be presented at the American Stroke Association's International Stroke Conference 2023.