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

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

Introductions Travis Frosch Director of Analytics and Cybersecurity GE Healthcare Digital

What IS Artificial Intelligence (AI)

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

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

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

AI timeline with examples

AI timeline with examples https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Double Clicking on Deep Learning

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

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

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

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.

What does Deep Learning look like in motion?

AI in Healthcare: Supporting not supplanting

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

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

A few use cases in Imaging

“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

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

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

Questions