1. Artificial Intelligence in Healthcare
The key to the future?
November 7, 2018

2. Today’s discussion Introductions What IS Artificial Intelligence (AI)
AI timeline with examples
Double clicking on Deep Learning
AI in Healthcare: Supporting not supplanting
A few use cases in Imaging
Questions

3. Introductions Travis Frosch Director of Analytics and Cybersecurity
GE Healthcare Digital

4. What IS Artificial Intelligence (AI)

5. What do you first think of when you hear “AI”?

6. ar·ti·fi·cial in·tel·li·gence noun the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages

7. Artificial Intelligence is…
…the ability of a machine to mimic or exceed human intelligence
Artificial Intelligence
Machine
Learning
Deep Learning

8. AI timeline with examples

9. AI timeline with examples

10. Double Clicking on Deep Learning

11. The Artificial Intelligence trajectory
2011 ~ 2nd Generation AI
Deep Learning
1956 ~ 1st Generation AI
1950
2000
Today
2050

12. How does Deep Learning work?
sky
person
kite
beach
Image
1,000 dimension description vector
Classification
Object ID

13. Moving from Algorithm driven to Data driven
Conventional Machine Learning
Deep Learning
Incomplete domain
knowledge from a human
Input data to be classified
(a set of pixels)
Algorithm
(feature)
Nodule
Non-nodule
Classifier
Domain knowledge
from data itself
Input data to be classified
(a set of pixels)
Neural
Network
Nodule
Non-nodule
Classifier
Dependent on data quality
Dependent on human knowledge VS
Improvements require new algorithm development
Improvements require better data collection
1st Gen. AI
2nd Gen. AI

14. Success rates on ImageNet Visual Recognition Challenge
SOURCE: ImageNet Standard Vision Lab, The Economist
Success rates on ImageNet Visual Recognition Challenge
2010
2011
2012
2013
2014
2015
72%
75%
85%
88%
94%
97%
Human recognition level = 95%
1st Gen. AI
2nd Gen. AI
And in just the past year, the evidence that deep learning beats expert systems has been mounting. Not only is second gen AI beating first gen AI – it is demonstrating the true definition of artificial intelligence by exceeding human intelligence.
Have a look at this graph from the ImageNet Challenge, designed by the Stanford Vision Lab. Since 2010, the competition has tested the ability of deep learning algorithms to successfully identify specific images from a growing library of over 10 million annotated photographs. In other words, can a machine correctly identify all the photos containing an elephant, or a motorcycles, or an apple? The baseline for a human to accurately perform the task is around 95%.
During the first two years of the competition, traditional rules based algorithms were used. And these were great results: 72% and 75%. But look what happened when a team from the University of Toronto shifted to a deep learning approach……and in 2015, a team from Microsoft in Beijing achieved 97% accuracy – surpassing the human.
And don’t think the evidence is limited to images on Facebook – in August of this year, a team in the Netherlands published data showing deep learning outperfoming first generation AI (e.g. CAD) for identification of breast malignancy on a dataset of 45,000 mammograms.

15. What does Deep Learning look like in motion?

16. AI in Healthcare: Supporting not supplanting

17. A fun example of how AI supports humans
Pull out your smart phone – go to AutoDraw.com

18. The market is reacting and the money is following
Healthcare IT News and HIMSS Analytics HIT Market Indicator:
Artificial Intelligence
The market is reacting and the money is following
Half of hospitals to adopt artificial intelligence within 5 years
Healthcare IT News and HIMSS Analytics survey

19. A few use cases in Imaging

20. “Soft” vs. “hard” use cases of AI in healthcare
Soft use case
Hard use case
For example, a “soft” use case would have a deep learning application raise a flag - “this case may be urgent” - while a “hard” use case would have it predict - “this tumor is benign.”
This case may be urgent
This tumor is benign

21. Imaging workflow efficiency example
Yes - Reacquire the image
Problem with
Image?
No -
Continue
Patient has a chest X-ray
Image acquired
Deep Learning algorithm fired
Classification determined

22. Imaging prioritization + notification example
No - Continue
Potential Critical
Finding?
No issues with image acquisition
Deep Learning algorithm fired
Classification determined
Yes – Trigger notification and move image to top of read queue

23. Questions