Artificial Intelligence: The Ethics and Abilities

Acute intracranial hemorrhage (ICH), sometimes referred to as a "brain bleed," shares symptoms with several other neurological conditions. Today, emergency departments rely on CT scans to detect this life-threatening condition--and even the most experienced radiologists can sometimes miss the subtle signs of the condition on such lower resolution images. Now, researchers from the University of California, San Francisco and the University of California, Berkeley have demonstrated that a deep learning artificial intelligence (AI) algorithm can provide "expert-level" detection of brain hemorrhage in a new study published in the Proceedings of the National Academy of Sciences--not only performing at the same standard as expert radiologists but finding tiny brain bleeds that those experts overlooked. The researchers used a single-stage, end-to-end, fully convolutional deep learning neural network in order to help identify what are usually very small abnormalities that must been detected on an image known for poor soft tissue contrast and low signal-to-noise issues. They trained the algorithm on a data set of over 4,000 CT exams where ICH abnormalities were manually highlighted at the pixel level.