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
SEARCH
Current Location:
>
> This Story

starstarstarstarstar (2)
Log in or Register to rate this News Story
Forward Printable StoryPrint Comment
advertisement

 

advertisement

 

Artificial Intelligence Homepage

Sectra showcases work-in-progress, AI functionality at RSNA Gives radiologist greater control over findings

Scalable AI utilization insights from Montefiore at RSNA Starting with patients at risk for respiratory failure

NYU releases biggest ever MR data set in AI Facebook collaboration With fastMRI, acceleration of imaging by factor of four 'already possible'

Subtle Medical closes RSNA with CE mark and FDA clearance of PET AI solution Speeds up scans by factor of four, enhanced image quality

Infervision showcases new AI concepts at RSNA Detecting four different conditions on one chest scan

Canon debuts AI for image reconstruction and 1.5T MR at RSNA Advanced Intelligent Clear-IQ Engine and Vantage Orian

Where will AI make its first major market impact in radiology? Four radiology experts share their views at RSNA

Aidoc and ACR announce partnership for AI in imaging Establishing a registry to better understand AI in the clinical setting

Arterys touts cloud-native platform and regulatory approval in 98 countries AI capabilities with 'unmatched' security

NVIDIA announces AI partnerships with OSU, NIH Using Clara to deploy algorithms designed in-house

Five key takeaways about AI from RSNA

by John W. Mitchell , Senior Correspondent
In a lively session that featured candid insights from Dr. Paul Chang, vice chair of radiology informatics at the University of Chicago, and two other panelists, an audience of radiologists, data scientists, informatics experts, and vendors learned several fundamental AI truths.

The RSNA session titled “Deep Learning & Machine Intelligence in Radiology” also included featured panelists Dr. Luciano Prevedello, chief of imaging informatics and a neuroradiologist at Wexner Medical Center and Abdul Hamid Halabi, global business development lead for Nvidia.

Story Continues Below Advertisement

Click to visit GE Healthcare Service Shop for ultrasound probe solutions

Get reliable ultrasound probe replacements, faster repair, and service coverage you can count on with GE Healthcare. Service Shop gives customers on-demand access to over 100 OEM new, used, and refurbished probe replacements.



Here are the five main takeaway points from the discussion:

1 – Here's what artificial intelligence actually means
AI is achieved when a computer mimics the activity of the human brain and eyes, learning not through human coding but rather through vast amounts of data review, according to Prevedello. AI scientists, including radiologists, walk computers through deep learning with progressive iterations. The result is error rates that are increasingly better than what humans can achieve. This makes the AI especially adaptable for finding anomalies, such as cancer lesions, strokes, or fractures sooner. AI also can eliminate variability in diagnosis from one well-trained radiologist to another.

2 – AI is neither new nor spooky
Deep learning that drives AI is a form of linear regression, a statistical tool that has been around for a long time. It doesn’t so much require cleverness as it does a lot of data to teach algorithms. Chang asserted that AI is not, “new or spooky.” However, the trend in AI machine learning is akin to a roller coaster ride and currently we are approaching the top. The climb to the peak is marked by nervousness and even fear – but there is also a lot of excitement. He predicted that many early vendors will not succeed. But he said these failures are necessary for the imaging AI sector to learn from its mistakes and advance.

3 – Taking a lesson from self-driving cars
Halabi said rapid advancements in the hardware used by gamers enabled AI development. He also played a video of an Nvidia self-driving car. It featured the “view” through the computer as it recognized other vehicles (data) and how this data behaved. The video illustrated that while most of what the self-driving car saw was normal traffic, it instantly recognized anomalies – just as imaging AI must.

The goal for Nvidia is to train their self-driving vehicle for the day that two deer jump into the road from opposite directions. The algorithm must navigate such a scenario with a perfect outcome to avoid a crash, property damage and death or injury to the passengers. For imaging, AI must detect even the rarest and most unlikely diseases and injuries.
  Pages: 1 - 2 >>

Artificial Intelligence Homepage


You Must Be Logged In To Post A Comment

Advertise
Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Directory
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Requests
Gold Service Dealer Program
Receive RFP/PS
Requests
Healthcare Providers
See all
HCP Tools
Jobs/Training
Find/Fill
A Job
Parts Hunter +EasyPay
Get Parts
Quotes
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
Quotes
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 ©2001-2018 DOTmed.com, Inc.
ALL RIGHTS RESERVED