IBM Watson expands solutions for healthcare

ibm-watson-for-healthcareIBM is set to preview new imaging solutions from Watson Health and Merge Healthcare to assist healthcare providers.

Announced at the Radiological Society of North America Annual Meeting (RSNA 2016), new imaging solutions are designed to improve patient diagnosis, treatment, and monitoring.

IBM researchers estimate that medical images account for at least 90 percent of medical data today. The main challenge is the fact that the volume of medical images can be huge. Radiologists in some hospital emergency rooms handle thousands of images each day.

“The depth of Watson-powered solutions on display at RSNA 2016 from Watson Health’s imaging group and from Merge are unmatched among the AI community, and showcase how IBM is bringing cognitive computing to healthcare in clinically meaningful ways,” said Anne LeGrand, vice president of Imaging for IBM Watson Health.

Watson Health will show:

A cognitive peer review tool to help healthcare professionals reconcile differences between a patient’s clinical evidence, and data in that patient’s electronic health record (EHR)

A cognitive data summarization tool  to provide radiologists, cardiologists, and other physicians with patient-specific clinical information to use when interpreting imaging studies, or when diagnosing and treating patients

A cognitive physician support tool  to help doctors personalize healthcare decisions based on integrating imaging data with other types of patient data

The MedyMatch “Brain Bleed” App, a cognitive image review tool to help emergency room physicians diagnose a stroke or brain bleed in a trauma patient by identifying relevant evidence in a patient record

Merge will show:

# Marktation, a new process for interpreting medical images to help physicians improve image reading speed and accuracy, with an initial application in mammography

Watson Clinical Integration Module, a cloud application for radiologists to help increase reader efficiency and counteract common causes of errors in medical imaging, such as base rate neglect, anchoring, bias, framing bias, and premature closure

Lesion Segmentation and Tracking Module, to help radiologists increase the speed by which they interpret and report comparison exams in cancer patients and for other patient conditions that require longitudinal tracking

Related News

Latest News

Latest News