Radiology

A deep learning system detected breast cancer in mammograms as well as experienced radiologists, according to a landmark study.

The European Union approved for clinical use an AI system that recognizes normal chest X-rays. ChestLink is the first autonomous computer vision system to earn the European Economic Area’s CE mark for medical devices...

The U.S. government’s largest national health insurance plan will pay for hospital use of a deep learning model, building momentum for AI to become an integral part of the doctor’s toolkit.

AI lately has achieved dazzling success interpreting X-rays and other medical imagery in the lab. Now it’s catching on in the clinic. Roughly one-third of U.S. radiologists use AI in some form in their work.

Some doctors don’t trust a second opinion when it comes from an AI system. A team at MIT and Regensburg University investigated how physicians responded to diagnostic advice they received from a machine learning model versus a human expert.

When you lack labeled training data, pretraining a model on unlabeled data can compensate. New research pretrained a model three times to boost performance on a medical imaging task.

The pandemic has pushed hospitals to their limits. A new machine learning system could help doctors make sure the most severe cases get timely, appropriate care.

U.S. government approval of medical AI products is on the upswing — but information about how such systems were built is largely unavailable. The U.S. Food and Drug Administration (FDA) has approved a a plethora of AI-driven medical systems.

A model designed to assess medical patients’ pain levels matched the patients’ own reports better than doctors’ estimates did — when the patients were Black.

AI accelerated the search for a coronavirus vaccine, detected Covid-19 cases, and otherwise softened the pandemic’s blow. Machine learning researchers worldwide scrambled to harness the technology against the coronavirus.

It’s expensive to pay doctors to label medical images, and the relative scarcity of high-quality training examples can make it hard for neural networks to learn features that make for accurate diagnoses.

Will AI that discriminates based on race, gender, or economic status undermine the public’s confidence in the technology? Seduced by the promise of cost savings and data-driven decision making, organizations will deploy biased systems that end up doing real-world damage.

Controversy erupted over the need for transparency in research into AI for medicine. Google Health introduced a system that purportedly identified breast cancer more accurately than human radiologists.

Convolutional neural networks are good at recognizing disease symptoms in medical scans of patients who were injected with iodine-based dye, that makes their organs more visible. But some patients can’t take the dye. Now synthetic scans from a GAN are helping CNNs learn to analyze undyed images.

Neural networks are famously bad at interpreting input that falls outside the training set’s distribution, so it’s not surprising that some models are certain that cat pictures show symptoms of Covid-19. A new approach won’t mistakenly condemn your feline to a quarantine.