Machine learning can read cardiac MRIs with the same accuracy as a physician, with much higher speed, according to a recent study published in Circulation: Cardiovascular Imaging and reported by Cardiovascular Business.
There’s plenty of research into the diagnostic accuracy of medical smartphone apps created to supply clinical decision support (CDS). However, few studies have looked at how helpful these apps are in clinical practice.
There’s still a long way to go with both research into Alzheimer’s disease and AI tools to help detect it, but deep-learning approaches continue to show promise for classifying the condition on images of the brain.
Minor disruptions in routine can cause serious setbacks in individuals with learning challenges and other neurological disabilities. The time may be ripe for combining AI with chaos theory to predict effects and outcomes.
Duke University researchers have used AI to boost the resolution of optical coherence tomography (OCT) to improve medical images across fields, from cardiology to oncology. Their findings were recently published in nature photonics.
Urinary tract infections make up a significant portion of microbiological screening in diagnostic laboratories, yet nearly two-thirds of samples come up negative. But AI has the potential to improve the process by reducing the number of query samples and enabling diagnostic services to concentrate on those that many have actual infections.