Reshaping Medical Imaging With Superior Capabilities of AI Radiology Solutions

Sifting through the volumes of patient information to identify relevant patterns is an almost impossible task for radiologists to undertake without a virtual assistant. AI in radiology technology is capable of offering immediate clinical decision support toward diagnosis through the meaningful interpretation of medical images and the culling of latent information to distinguish patterns, which can considerably enhance the work of radiologists.

The traditional method relies on the radiologist to read a scan with little or no help, with a full and final dependency on the identification of the radiologist. With our AI radiology technologies, a chest Computed Tomography (CT) scan is reviewed and potential findings are immediately identified from the image. Additionally, by combing through the patient history, related to the particular anatomy scanned, identification is made simpler.

Clinicians are forced to depend on medical image analysis performed solely by radiologists. Through our image analysis solutions in AI for radiology, computer vision software is powered with deep learning algorithms to undertake automated analysis, with highly accurate results that are delivered much faster than physical processes.

Radiologists are prone to errors due to imaging and human constraints.  A misdiagnosis is a natural outcome of overloaded radiologists. Our breakthrough technology in AI for radiology offers deep learning and replicates the functions of the human brain to recognize images with higher than human level accuracy.

Through the use of comprehensive research applications, our intelligent algorithms are embedded with the ability to identify and analyze textures in medical images that are susceptible to be missed by the human eye. Computer-aided diagnosis has never been easier through the merger of AI and radiology.

Categorizing medical images is currently limited to the physical labelling of medical data. Our data augmentation techniques within AI and radiology curtail the memorization of training data and force the use of performance data that lies outside the training set. Augmentation in medical imaging creates a variety of scenarios that enhances the quality of training.