Specialized electronic circuits called graphic processing units, or GPUs, are at the heart of modern mobile phones, personal computers and gaming consoles. By combining multiple GPUs in concert, researchers can solve previously elusive image processing problems. For example, Google and Facebook have both developed extremely accurate facial recognition software using these new techniques. GPUs are also crucial to radiologists, because they can rapidly process large medical imaging datasets from CT, MRI, ultrasound and even conventional x-rays. Now some radiology groups and technology companies are combining multiple GPUs with artificial intelligence (AI) algorithms to help improve radiology care.
There's a clear trend that having more data makes it easier to train artificial intelligence. Bigger datasets, like ImageNet, originally showed that AI could be useful for tasks like image recognition, leading to a race among everyone from large technology companies to academics to compile new datasets to stretch the limits of AI.
Machine learning (ML) is causing quite the buzz at the moment, and it's having a huge impact on healthcare. Payers, providers, and pharmaceutical companies are all seeing applicability in their spaces and are taking advantage of ML today. This is a quick overview of key topics in ML, and how it is being used in healthcare. A machine learning model is created by feeding data into a learning algorithm. The algorithm is where the magic happens.
In the case of CheXnet, the research team led by Stanford adjunct professor Andrew Ng, started by training the neural network with 112,120 chest X-ray images that were previously manually labeled with up to 14 different diseases. One of them was pneumonia. After training it for a month, the software beat previous computer-based methods to detect this type of infection. The Stanford Machine Learning Group team pitted its software against four Stanford radiologists, giving each of them 420 X-ray images. This graphic shows how the radiologists–represented by the orange Xs–did compared to the program–represented by the blue curve.