
AI spots anomalies in medical images
The neural network detects anomalies in medical images more successfully than general-purpose solutions.
The neural network detects anomalies in medical images more successfully than general-purpose solutions.
More than 20 hospitals from across the world together with NVIDIA have used AI to predict Covid patients’ oxygen needs on a global scale.
Research using machine learning on images of everyday items is improving the accuracy and speed of detecting respiratory diseases, reducing the need for specialist medical expertise.
Researchers discovered that AI models have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings.
Trained to see patterns by analyzing thousands of chest X-rays, a computer program predicted with up to 80 percent accuracy which COVID-19 patients would develop life-threatening complications within four days.
A study from Stanford University found limitations in the Food and Drug Administration’s approval process.
Using machine learning, researchers have developed a new computational tool to screen patients with common but blinding retinal diseases, potentially speeding diagnoses and treatment.
Researchers have developed a new AI platform that detects COVID-19 by analyzing X-ray images of the lungs.
Radiologists are investigating people's medical conditions and pregnancies remotely thanks to an ESA-backed robotic technology.
Researchers have developed a predictive artificial intelligence model that can tell the difference between healthy patients, those who are ill with pneumonia and those who have COVID-19, from chest X-rays.
behold.ai has been issued with a CE Mark Class lla certification in the UK and EU for its AI-based technology that can diagnose chest X-rays as ‘normal’.
Radiologists assisted by deep learning based software were better able to detect malignant lung cancers on chest X-rays.
Wireless body sensors could replace the tangle of wire-based sensors that currently monitor babies in hospitals’ NICU and pose a barrier to parent-baby cuddling and physical bonding.
In a matter of seconds, a new algorithm read chest X-rays for 14 pathologies, performing as well as radiologists in most cases, a Stanford-led study says.
A "Hive Mind" of doctors, moderated by AI algorithms, makes more accurate diagnoses than the doctors or machine learning alone, according to a new study from Stanford and Unanimous AI.