Radiological sciences in the last ten years have advanced in a revolutionary manner, especially when it comes about medical imaging and computerized medical image processing. These techniques help in the understanding of the disease as well as initiation and evaluation of ongoing treatment. Apart from this, the dataset of these images is used in further analysis of such diseases occurring around the world as a whole. Heather Landi, a senior editor at Fierce Healthcare, writes in an article that IBM researchers estimate that medical images, as the largest and fastest-growing data source in the healthcare industry, account for at least 90 percent of all medical data. We can use a computer to process and manipulate the multidimensional digital images of psychological structures in order to visualize hidden characteristic diagnostic features that are very difficult or perhaps impossible to see using planer imaging methods.

Eyes are more than the "windows to the soul." As such, ocular health and neurological health are intertwined. The most skilled ophthalmologists can read ocular scans to not only look for eye disease, but also traces of a host of neurological disorders. Voxeleron is using artificial intelligence and machine learning to, as they put it, "democratize expertise." Their algorithms hold the promise of delivering expert-level diagnostic capabilities to any lab with a scanning device.

The American Association for Artificial Intelligence (AAAI) held its 1994 Spring Symposium Series on 19-23 March at Stanford University, Stanford, California. This article contains summaries of 10 of the 11 symposia that were conducted: Applications of Computer Vision in Medical Image Processing; AI in Medicine: Interpreting Clinical Data; Believable Agents; Computational Organization Design; Decision-Theoretic Planning; Detecting and Resolving Errors in Manufacturing Systems; Goal-Driven Learning; Intelligent Multimedia, Multimodal Systems; Software Agents; and Toward Physical Interaction and Manipulation. Proceedings of most of the symposia are available as technical reports from AAAI. There is a growing community of computer vision researchers who are working on medical applications. This interdisciplinary activity is in part application driven and related to the widespread proliferation of highresolution medical imagers.

Deep learning tech is making itself at home in hospitals by helping radiologists examine medical scans for just a buck per image. Now, GE Healthcare is bringing that AI tech directly to the scanners, thanks to partnerships with NVIDIA and Intel. It announced that it will update 500,000 of its medical devices around the world with NVIDIA AI tech, most notably its Revolution Frontier CT scanner (below). The tech "is expected to deliver better clinical outcomes in liver lesion detection and kidney lesion characterization because of its speed," GE wrote in a press release. The tech will also be used in GE's advanced ultrasound imaging devices to provide visualization and quantification of data. "NVIDIA's GPUs accelerate reconstruction and visualization of blood flow and improve 2D and 4D imaging for ... interventional deployments," the company said.