dismiss

Clean Sweep Live Auction on Wed. May 1st. 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 Pediatrics
SEARCH
Current Location:
>
> This Story


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

 

advertisement

 

CT Homepage

First ultra high-res CT scan performed on US patient Scanner at UC Davis can image anatomy as small as 150 microns

Trice Imaging connects imaging devices of large chain healthcare provider Aleris Patients and physicians can view images on laptops, cell phones

Industrial hi-res X-ray yields greater insight into child abuse case Identified microscopic injuries that would not have been detected with standard CT

Reducing extravasations in CT contrast-enhanced IV injections Tips and best practices for administering better care

House bill would require Medicare to cover CT colonography Supporters say it would improve screening compliance and outcomes

New approach identifies lung cancer patients most likely to respond to chemotherapy Combines radiomics and CT image assessment

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

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

Siemens to unveil its SOMATOM go.Top Cardiovascular Edition CT at ACC 19 Ideal for the cardiovascular outpatient setting

Hitachi unveils new CT and ultrasound solutions at ECR Standard version of SCENARIA VIEW and three new Arietta ultrasound solutions

Researchers used a malware attack
to deceive both radiologists and
AI algorithms into misdiagnosing
CT scans

Researchers orchestrate malware attack to expose imaging vulnerabilities

by John R. Fischer , Staff Reporter
A radiologist can misdiagnose a scan for a number of reasons. Maybe they missed an abnormality, or judged the test as normal when there was actually an issue, or identified one problem but not another. And while the introduction of artificial intelligence and machine learning has instilled greater confidence in diagnoses, does the addition of these tools mean that radiologists can’t still get the wrong result? Or even be deceived into getting the wrong one?

Cybersecurity researchers at Ben-⁠Gurion University of the Negev in Israel are saying yes to this last question, after using a malware attack to trick both radiologists and artificial intelligence algorithms into making wrong diagnoses in a series of CT scans.

Story Continues Below Advertisement

RaySafe helps you avoid unnecessary radiation

RaySafe solutions are designed to minimize the need for user interaction, bringing unprecedented simplicity & usability to the X-ray room. We're committed to establishing a radiation safety culture wherever technicians & medical staff encounter radiation.



“Many researchers have warned that AI cannot be fully trusted because they can be easily fooled (especially in the case of image recognition),” Dr. Yisroel Mirsky, lead researchers in the BGU department of software and information systems engineering (SISE) and project manager and cybersecurity researcher at BGU’s National Cyber Security Research Center, told HCB News. “Researchers are currently working hard to make these algorithms robust to 'adversarial attacks'. But until then, we have the responsibility to double check the decisions of AI to ensure that there is no foul play, and use medical AI as a tool for assisting the detection process, but not replace it.”

Using two 3D-conditional, generative adversarial networks (GAN), the researchers – with permission – hacked into a hospital’s internal network, and proceeded to add or remove malignant lung cancer findings and replace them with medical imagery from the internet. They then hired three radiologists to make diagnoses for 70 tampered and 30 authentic CT scans.

The three misdiagnosed 99 percent of healthy scans inserted with cancer imagery as malignant, and 94 percent of patients with cancer as healthy, even after applying algorithms to remove malignancies from the scans of those with cancer.

Once informed of the attack, the radiologists were still unable to tell the difference between the tampered and authentic images, misdiagnosing 60 percent of those with images inserted, and 87 percent of those that had cancers removed.



While confident that mistakes such as these will encourage providers to seek out more ways to combat cyberattacks, Mirsky does not see them reaching the same level of protection as other industries anytime soon.
  Pages: 1 - 2 >>

CT 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 ©2001-2019 DOTmed.com, Inc.
ALL RIGHTS RESERVED