Global artificial intelligence (AI) is forecast to reach $200 billion by 2022, and if the current trend holds, health care will make up a significant portion of that market. That’s no surprise, given its promise — AI has the potential to reduce administrative costs, cut down on patient wait times, and diagnose diseases. And today, Intel and Philips demonstrated two more applications: bone modeling and lung segmentation.

Philips Medical, Philips’ medical supply and sensor division, published the results of recent machine learning tests performed on Intel’s Xeon Scalable processors with its OpenVINO computer vision toolkit. Researchers explored two use cases: one on X-rays of bones to model how bone structures change over time, and the other on CT scans of lungs for lung segmentation (i.e., identifying the boundaries of lung from surrounding tissue).

They achieved a speed improvement of 188 times for the bone-age-prediction model, which went from a baseline result of 1.42 images per second to a rate of 267.1 images per second. The lung-segmentation model, meanwhile, saw a 38 times speed improvement, processing 71.7 images per second after optimizations, up from 1.9 images per second.

“Intel Xeon Scalable processors appear to be the right solution for this type of AI workload,” said Vijayananda J., chief architect at Philips HealthSuite Insights. “Our customers can use their existing hardware to its maximum potential … while still aiming to achieve quality output resolution at exceptional speeds.”

Intel contends that its processors, rather than the powerful graphics cards…