News | Artificial Intelligence | February 06, 2019

IMS to Unveil Prototype Imaging Machine Learning Platform at HIMSS19

Platform will employ Google Cloud Machine Learning Engine to help triage imaging studies that need immediate attention

IMS to Unveil Prototype Imaging Machine Learning Platform at HIMSS19

February 6, 2019 — International Medical Solutions (IMS) announced a solution that will enable radiologists and other clinicians to use machine learning (ML) modeling to triage studies and focus on medical images that need immediate attention. The platform will use a standard file format, enabling any artificial intelligence (AI) company to add their own model to the workflow.

The prototype, which will be unveiled at the 2019 Healthcare Information and Management Systems Society (HIMSS) conference, Feb. 11-15 in Orlando, Fla., will feature two common use cases. The first scenario uses Google Cloud ML Engine to provide an indicator for prioritization. Using the example of a radiologist who has several hundred chest cases to review, the study list will leverage machine learning to provide an indicator of the cases that are most likely to have a pathology. This will enable the radiologist to focus on those cases quickly and expedite the results. The second scenario uses Google Cloud ML Engine to provide better decision support. In situations where there is no urgency involved with a diagnosis, such as determining breast density, machine learning modeling can be used for clinical decision support.

For more information: www.imstsvc.com

Related Content

Of all the buzzwords one would have guessed would dominate 2020, few expected it to be “virtual”. We have been virtualizing various aspects of our lives for many years, but the circumstances of this one has moved almost all of our lives into the virtual realm.

Getty Images

Feature | Radiology Education | September 18, 2020 | By Jef Williams
Of all the buzzwords one would have guessed would dominate 2020, few expected it to be “virtual”.
As the silos of data and diagnostic imaging PACS systems are being collapsed and secured, the modular enterprise imaging platform approach is gaining significance, offering systemness and security
Feature | Coronavirus (COVID-19) | September 18, 2020 | By Anjum M. Ahmed, M.D., MBBS, MBA, MIS
COVID-19 is now everywhere, and these are the lo
Cloud and cloud-native architecture is the future for computing solutions in EI applications

Getty Images

Feature | Enterprise Imaging | September 18, 2020 | By Henri “Rik” Premo
With over five years of presence in the rapidly expanding...
News | Artificial Intelligence | September 16, 2020
September 16, 2020 — Konica Minolta Healthcare Americas, Inc.
Change Healthcare announced innovative new artificial intelligence (AI) models, trained by expert physicians, which extract meaningful diagnostic information from text in EHRs. The first application of this technology will be within the InterQual AutoReview solution, which automates medical necessity reviews using real-time data from EHRs.
News | Artificial Intelligence | September 14, 2020
September 14, 2020 — Change Healthcare announced innovative new...
The National Imaging Informatics Course-Radiology (NIIC-RAD) Term 1 will be held online September 28 - October 2, 2020. NIIC-RAD is made possible through a partnership between the Radiological Society of North America (RSNA) and the Society for Imaging Informatics in Medicine (SIIM)

Getty Images

News | Radiology Education | September 11, 2020
September 11, 2020 — The...
The Radiological Society of North America (RSNA) has launched its fourth annual artificial intelligence (AI) challenge, a competition among researchers to create applications that perform a clearly defined clinical task according to specified performance measures. The challenge for competitors this year is to create machine-learning algorithms to detect and characterize instances of pulmonary embolism.

Getty Images

News | Artificial Intelligence | September 11, 2020
September 11, 2020 — The Radiological Society of North America (RSNA...
Six months after deployment, the no-show rate of the predictive model was 15.9%, compared with 19.3% in the preceding 12-month preintervention period — corresponding to a 17.2% improvement from the baseline no-show rate (p < 0.0001). The no-show rates of contactable and noncontactable patients in the group at high risk of appointment no-shows as predicted by the model were 17.5% and 40.3%, respectively (p < 0.0001).

Weekly outpatient MRI appointment no-show rates for 1 year before (19.3%) and 6 months after (15.9%) implementation of intervention measures in March 2019, as guided by XGBoost prediction model. Squares denote data points. Courtesy of the  American Roentgen Ray Society (ARRS), American Journal of Roentgenology (AJR)

News | Artificial Intelligence | September 10, 2020
September 10, 2020 — According to ARRS’