Clean Sweep Live Auction on Thur. March 28th. Click to view the full inventory

DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Mobile Imaging
Current Location:
> This Story

Log in or Register to rate this News Story
Forward Printable StoryPrint Comment




Artificial Intelligence Homepage

Apple study suggests wearable technology may be useful in detecting atrial fibrillation May assist in stroke and hospitalization prevention

Nvidia unveils Clara AI platform at GPU Technology Conference Equipped with 13 state-of-the-art classification and segmentation algorithms

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

Beyond the hype: How practical AI is enhancing radiology Insights from Imad B. Nijim, chief information officer for MEDNAX Radiology Solutions

Machine learning reduces false positives for lung cancer in low-dose CT False positives occur at rate of 96 percent

Siemens and ESR present first Digital Experience Hall at ECR Aims to stimulate exchange of thoughts and knowledge about digitization in radiology

Canon showcases CT image reconstruction tech and software upgrade at ECR Removing noise while preserving signals

Personalized cardiac test could eliminate unnecessary catheterizations Examines flow of blood with AI and CT

GE debuts work-in-progress algorithms at ECR 2019 For predicting no-shows and detecting the presence of pneumothoraces

Women's brains appear three years younger than men's at the same age: PET study A machine-learning algorithm assisted with the analysis

How will radiologists access AI?

From the November 2018 issue of DOTmed HealthCare Business News magazine

By Dr. Ulrik Kristensen

It is already clear that AI will play a role in almost every aspect of medical imaging.
Operational analytics, data acquisition and dose monitoring, scheduling and workload optimization, automatic tissue segmentation and measurement tools, automatic image analysis and diagnostic reporting are all active targets for new AI tools. Machine learning image analysis algorithms are being developed for a diverse range of clinical applications, such as neurological disorder detection, lung nodule detection and breast cancer diagnosis. Until recently, these algorithms were developed mainly at university hospitals for internal research use only, as well as a handful of specialist image analysis companies. But with the AI startup community flourishing and the number of commercially available algorithms increasing, how these tools will be integrated in the radiologist workflow remains unclear. In this article, we review and discuss the different integration strategies for AI and propose how the algorithm deployment maturation will most likely materialize.
Story Continues Below Advertisement


Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.

Integration strategies
AI adoption and integration will depend heavily on the broader imaging IT structure and healthcare IT architecture. Today, radiology IT is still largely departmental. Hospital consolidation in the U.S. and some Western European countries is starting to drive enterprise imaging, and is creating shared resources between radiology departments within the same health system, or access to imaging data for departments outside radiology. On-premise data storage is, however, still the most commonly used architecture, though some hospitals in the U.S. and Western Europe are starting to adopt public cloud solutions as security concerns decrease and providers get more comfortable with public cloud storage, after some years of hybrid cloud for backup and disaster recovery.

The highly departmental structure of radiology and slow adoption of cloud has also had consequences for the development of AI, and initial development of radiology AI was limited to academic research projects within single hospitals. More recently, as the AI industry has proliferated outside of these early developers, it has remained limited by the confines of existing healthcare IT frameworks. This has led to a mixture of integration strategies today:

PACS integration
Although enterprise imaging is in demand, particularly in the U.S., PACS is still the most widely used viewing platform in medical imaging. Many AI vendors have attempted to make their algorithms easily accessible and integrated into the radiologist workflow by partnering with PACS vendors for integration into their software and clinical applications. The AI software would typically launch through a button in the PACS user interface; although integration could be even tighter, this is still a step forward compared to individually installed algorithms requiring separate login. A strong motivation from the AI vendors’ perspective is to tap into the PACS vendors’ distribution network while keeping the costs of doing business at a minimum.
  Pages: 1 - 2 - 3 - 4 >>

Artificial Intelligence Homepage

You Must Be Logged In To Post A Comment

Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Gold Service Dealer Program
Receive RFP/PS
Healthcare Providers
See all
HCP Tools
A Job
Parts Hunter +EasyPay
Get Parts
Recently Certified
View Recently
Certified Users
Recently Rated
View Recently
Certified Users
Rental Central
Rent Equipment
For Less
Sell Equipment/Parts
Get The
Most Money
Service Technicians Forum
Find Help
And Advice
Simple RFP
Get Equipment
Virtual Trade Show
Find Service
For Equipment
Access and use of this site is subject to the terms and conditions of our LEGAL NOTICE & PRIVACY NOTICE
Property of and Proprietary to DOTmed.com, Inc. Copyright ©2001-2019 DOTmed.com, Inc.