Though the idea of artificial intelligence displacing radiologists worries more than half of surveyed medical students interested in an imaging career, radiology programs have seen a spike in applications in recent years, according to work published in Academic Radiology.

A recently discovered property of spider silk known as supercontraction could one day comprise a key building block of artificial muscles and robotic actuators, according to research published March 1 in Science Advances.

A report released by Portland-based firm Big Market Research on March 6 projects the artificial intelligence in medicine market will grow to $18.12 billion by 2025, advancing at a compound annual growth rate (CAGR) of 49.6 percent.

AI systems can detect breast cancer just as well as radiologists, according to a study published March 5 in the Journal of the National Cancer Institute.

Amazon has extended a grant valued at $2 million to Harvard Medical School so the medical giant can experiment with machine learning and AI to streamline their clinical workflow, Bloomberg reported March 4.

FDA Commissioner Scott Gottlieb is resigning. Gottlieb has manned the agency since May 2017 and will leave his position in about a month.

Researchers from the Icahn School of Medicine at Mount Sinai have developed an AI platform that’s reportedly capable of detecting a range of neurodegenerative diseases in human brain tissue samples, according to a study published in the February issue of Laboratory Investigation.

Forty percent of so-called AI startups in Europe don’t actually use AI programs in their products, the Financial Times reported March 4.

Using big data and AI-driven prediction models can be clinically useful, but it’s also important to learn about that data and the processes involved in collecting it, according to work published in JAMA Psychiatry.

Representatives from a handful of major global imaging societies are collaborating on a “living document” that will outline a clearer set of ethics for the use of AI in radiology.

Zebra Medical Vision (Zebra-Med), an Israel-based deep learning imaging analytics company, announced it was granted CE certification for two of its AI-based products intended to speed up clinical review and diagnosis. One is for pneumothorax in chest x-rays and the other helps radiologists detect brain bleeds in CT scans.

Researchers at the Children’s Hospital of Philadelphia (CHOP) developed machine learning models that can detect the presence of sepsis in infants, hours before physicians. Findings from the study were published in PLOS One.