Artificial intelligence

Artificial intelligence has unimaginable potential to revolutionize medicine. We cover the latest technology breakthroughs of machine learning and deep learning algorithms that process mindboggling amounts of data, spot even the smallest detail in medical images and help medical professionals in designing treatment plans.

AI accurately detects COVID-19 on chest x-rays

AI accurately detects COVID-19 on chest x-rays

Researchers have developed a new AI platform that detects COVID-19 by analyzing X-ray images of the lungs.

COVID-19: AI system monitores vital health

COVID-19: AI system monitores vital health

A key symptom of COVID-19 – oxygen saturation – is now being estimated remotely from a camera, thanks to research from University of South Australia (UniSA).

AI uncovers missing info about ethnicity in population health

AI uncovers missing info about ethnicity in population health

Machine learning can be used to fill a significant gap in Canadian public health data related to ethnicity and Aboriginal status, according to research by a University of Alberta research epidemiologist.

Deep learning enables screening for eye disease

Deep learning enables screening for eye disease

Researchers created a novel deep learning method that makes automated screenings for eye diseases such as diabetic retinopathy more efficient.

Sorting out viruses with machine learning

Sorting out viruses with machine learning

Scientists develop a label-free method for identifying respiratory viruses based on changes in electrical current when they pass through silicon nanopores.

Next-generation computer chip for AIs

Next-generation computer chip for AIs

Engineers have developed a next-generation circuit that allows for smaller, faster and more energy-efficient devices – which would have major benefits for AI systems.

Machine learning predicts anti-cancer drug efficacy

Machine learning predicts anti-cancer drug efficacy

With the advent of pharmacogenomics, machine learning research is well underway to predict patients' drug response that varies by individual from the algorithms derived from previously collected data on drug responses.

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