In this Intel on AI podcast episode: Before an MRI technologist can scan a patient, they manually specify the slices they want the MRI to acquire. This can take several minutes of tweaking, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan. It can also introduce inconsistencies into images taken over time if parameters or positioning are slightly different each time a patient gets scanned, making it challenging to accurately monitor disease progression or treatment. Recording live at the Intel AI Summit event in San Francisco California, Matthew DiDonato Director of Product and AI at GE Healthcare, joins the Intel on AI Podcast to talk about GE Healthcare’s AIRx solution. He highlights how AIRx uses deep learning to automatically identify anatomical structures to prescribe slice locations, and angle of those slices for neurological exams, delivering consistent and quantifiable results. Matthew explains how AIRx enables consistent, repeatable scan alignment to help physicians better monitor a patient across longitudinal studies and also reduces the amount of time a patient has to wait and spend during their MRI treatment. He also talks about how working with the Intel Distribution of OpenVino enabled GE to achieve a significant reduction in processing time to enable more efficient healthcare and better patient outcomes when using AIRx. Matt also talks about how GE Healthcare and Intel are working together on a number of other projects based on the GE Edison AI platform and achieving amazing result with Intel AI technology.

To learn more, visit:
gehealthcare.com

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

Tags: AI, artificial intelligence, deep learning, GE Edison AI, GE Healthcare, GE Healthcare AIRx, Intel AI Summit, machine learning, MRI, natural language processing, Supercomputing
 
Posted in: Artificial Intelligence, Audio Podcast, Healthcare, Intel, Intel on AI