As the evolution of healthcare has started becoming more and more prevalent through Artificial Intelligence, deep learning has also begun to carve out a place of its own specific place in the industry.

Mark Michalski, MD, executive director of the Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science stated that the anticipation regarding AI is due to three key factors: the increasing contribution of digital data that can be created, the creation of algorithms that make artificial neural networks and "GPU" chip architecture (graphics processing unit), developed by NVIDIA.

Deep learning as the rudimentary idea behind many recent developments for GPUs and people are utilizing it for various types of data including images, audio recordings, videos and text.

"The only reasons we are developing our own data center with GPUs because we depend on medical data such as MRIs and CTs, which is a lot of data and is sensitive because of privacy," Michalski explained. "We can nourish the GPU information derived from medical records, so that we can learn from our records."

Deep learning and Graphic Processing Units have big scope for how healthcare can be handled and interpret imaging and other clinical data.

“Sometimes in a day, the radiologist has to go through thousands of images. Having an advanced technology which can help to notice an abnormality in a stack of normal images, and further, automatically check that abnormality like a tumor or a dilated heart chamber, could be a huge productivity advantage for us” stated Michalski

Massachusetts General and Brigham and Women's Hospitals are now utilizing deep learning to self-act the tasks that are time-consuming. This has led to hospitals recognizing other positive uses of AI such as noticing details that sometimes bypass the human eye.