The new platform, affectionately called ‘Herman,' analyzes complex patterns in images of pathogen and human cell interactions, and can do so in a fraction of the time normally required.

Kai-Fu Lee, the CEO of a Chinese venture capital firm and former president of Google China, discussed AI and its impact on various sectors of the economy in a new interview with Fox Business.

Testing a previously developed deep-learning algorithm for assessing children’s bone age on x-rays, Harvard researchers have found their tool combined with a radiologist beats three competitors—AI alone, a radiologist alone and a pooled group of unaided experts.

It’s all about the data. We’ve been saying this for years. We can choose to look at this in one of two ways. It’s either a constant truism or it actually evolves and gains mass over time. In the age of artificial intelligence, it is both. 

Not just for years but for decades, the department of radiology at the University of Wisconsin School of Medicine and Public Health in Madison has been leading the charge on creating innovative technology and translating imaging research into clinical practice.

Countless predictions have been made about artificial intelligence and machine learning changing imaging screening and diagnosis at the point of patient care—and clinical studies and experience are now proving it. Radiologists say the impact is real in improving diagnosis of cancers and quality of care, consistency among readers and reducing read times and unnecessary biopsies. One shining example targets the evaluation of breast ultrasound imaging.

Smart technologies are often touted as the answer to some of cardiology’s greatest challenges in patient care and practice. But where does hyperbole end and reality begin with artificial intelligence, machine learning and deep learning?

Developments in vastly scalable IT infrastructure will soon increase the rate at which machine learning systems gain the capacity to transform the field of medical imaging across clinical, operational and business domains. Moreover, if the pace seems to be picking up, that’s because data management on a massive scale has advanced exponentially over just the past several years.