In this Intel on AI podcast episode: The process of diagnosing a patient with chest abnormality is done by radiologists and doctors with a lot of experience and expertise. This involves looking for the presence of foreign bodies, infiltrates, and other information to determine the type of infection so that proper medication can be suggested for a cure. This process can be challenging for providers with heavy workloads and sometimes expertise may not be available in remote areas. Alberto Gutierrez Ph.D. Chief Data Scientist, Analytics COE for HCL America, joins the Intel on AI podcast to talk about how HCL's Diagnostic Decision Support for Medical Imaging (DDSM) solution utilizes the power of deep learning to detect the presence of thoracic disease in patients Chest X-ray. He highlights how using the Intel Distribution of OpenVINO toolkit enables HCL to deliver optimized image processing to their customers driving clear ROI in processing and accurate image detection for patients. Alberto describes how this heightened performance assists radiologist to classify the type of infection present in the patient's chest X-ray, both saving waiting time and improving accuracy in patient diagnoses. He also talks about how HCL has worked closely with the Intel AI Builders program to utilize Intel support and software to achieve incredible performance improvements and provide greater value to their customers.

To learn more, visit: hcltech.com builders.intel.com/ai/solutions

Visit Intel AI Builders at: builders.intel.com/ai