Data-Driven Innovation and AI in Healthcare

© 2018 Irfan Essa, Georgia Tech, All Rights Reserved
Data-Driven
Innovation
Irfan Essa
Georgia Institute of
Technology
irfan@gatech.edu
irfanessa.com | @irrfaan
These slides available from: http://bit.ly/IESpineSummit20

“Did not know I was
invited to convince
you that AI is real”
Irfan Essa
Technology
irfan@gatech.edu

➢Google (2011 - Present)
➢Funded by
○ NSF / DARPA / NIH, etc.
○ Google, Facebook, Microsoft Intel, IBM,
Honeywell, Mitsubishi, Siemens, etc.
○ Humana, etc.
➢Advisor
○ Nidus, etc.
➢Today I am here as a Professor
○ Opinions are MY OWN!

➢A quick overview of
○ Artificial Intelligence,
○ Machine Learning,
○ Data Science, etc.
➢The three Cs (well kinda!)
○ Cloud, Crowd, Compute
➢Pitfalls, Warnings, and a good
Future
Today - Data Driven Innovation

Aggregation
to
Sensemaking
(Data to Inference)

Hypothetical Example
X
Y
Average Age
Dollar
Spent
Regression
Function
Approximation
Linear or Non-
Linear
y = mx + c

X
Y
Created a Model
Regression
Function
Approximation
Linear or Non-
Linear
Predictions
Decisions
y = mx + c

X
Y Clustering
Given Labels /
Annotations
Discovering
Patterns

Terminology
(Basics)

Artificial Intelligence
Augmented Intelligence
Anticipatory Intelligence
Collaborative Intelligence
Collective Intelligence
Humanistic Intelligence
Interactive Intelligence
Machine Intelligence
Symbiotic Intelligence
Augmented Intelligence
Anticipatory Intelligence
Collaborative Intelligence
Collective Intelligence
Humanistic Intelligence
Interactive Intelligence
Machine Intelligence
Symbiotic Intelligence
“Intelligence”
Knowledge Discovery
Cognitive
Perceptive
Reactive
Adaptive
Machine Learning
Pattern Recognition
Perception /
Sensing / Data
to
Action / Decision

Historically
➢Minsky/McCarthy (1956)
○ Newell, Simon, Papert, Winston, etc. etc.
➢Bush (1945) “As we may think”
➢Turing (1950) “Turing Test”
➢Licklider (1960) ”Man-Computer
Symbiosis”
➢Negroponte (1967) “Intelligent Agent”
➢etc.

“In computer science, artificial intelligence (AI),
sometimes called machine intelligence, is
intelligence demonstrated by machines, in
contrast to the natural intelligence displayed
by humans and other animals. Computer
science defines AI research as the study of
"intelligent agents": any device that perceives
its environment and takes actions that
maximize its chance of successfully achieving
its goals.”
https://en.wikipedia.org/wiki/Artificial_intelligence
➢ Formal Reasoning /
Philosophy
➢ Turing
➢ McCarthy/Minsky (1956)

Machine Learning
“Scientific study of algorithms and statistical
models that computer systems use to
effectively perform a specific task without using
explicit instructions, relying on patterns and
inference instead. It is seen as a subset of
artificial intelligence. Machine learning
algorithms build a mathematical model of
sample data, known as "training data", in order
to make predictions or decisions without being
explicitly programmed to perform the task.”
https://en.wikipedia.org/wiki/Machine_learning
➢ Arthur Samuel, coined the term
"Machine Learning" in 1959
➢ Turing Code Breaker …
➢ Logistic Regression ….

Pattern Recognition
“Pattern recognition is the automated
recognition of patterns and regularities in
data. Pattern recognition is closely related to
artificial intelligence and machine learning,
together with applications such as data mining
and knowledge discovery in databases (KDD),
and is often used interchangeably with these
terms”
“ML is one approach to pattern recognition,
while other approaches include hand-crafted
(not learned) rules or heuristics”

Data Science
“Data science is a multi-disciplinary field that
uses scientific methods, processes, algorithms
and systems to extract knowledge and insights
from structured and unstructured data. Data
science encompasses the fields of data mining
and big data.”
“The term "data science" has appeared in
various contexts over the past thirty years but
did not become an established term until
recently”

18
ML/AI
Foundations
Applications
Foundations
Applications
Dynamic Data
Neural
Computing
Anomaly
Detection
Interactive
ML/AI
Medical/Health
Logistics &
Manufacturing
Robotics
Social
Computing
Information
Security
Financial
Technologies
Education Data
Education
Education
AI + Cognition
Ethics / Data Bias
/ Policy
Energy &
Sustainability
High
Performance
Computing
Systems for ML,
ML for Systems
Computer Vision
Internet of
Things (Ubiq.)

Five Tribes of ML/AI
Symbolists
Connectionists
Bayesians
Analogizers
Evolutionaries
Need initial knowledge with data to
make inferences
Evolves mimicking nature (GA) Connections between neurons (NN)
Infer similarities (SVM, kNN) Incorporates new evidence (Bayesian)
Connectionists
Needs a lot of a
Annotated Data

Why Machine Learning / AI?
❖ The machine learning market size is expected to grow from USD 1.41
Billion in 2017 to USD 8.81 Billion by 2022, (Compound Annual Growth
Rate (CAGR) of 44.1%) [1].
❖ Revenue generated from the direct and indirect application of AI to
grow from $643.7 million in 2016 to $36.8 billion by 2025. (CAGR of 56.8%)
[2].
❖ Nearly all big tech companies have an artificial intelligence project, and
they are willing to pay experts millions of dollars to help get it done [3].
22
[1]: Markets and Markets Research Report TC5578, 9/2017
[2]: Tractica Research Report 3Q 2016
[3]: New York Times Technology, 10/22/2017
❖ Bezos, CEO of Amazon on future of AI, “I think it’s gigantic”
❖ Pichai, CEO of Google, “Future of Google is AI”.
❖ Gates, Founder, Microsoft , “AI can be our friend”
❖ Zuckerberg, Founder, Facebook. “AI is going to make our lives better in the future”

Top 5 Sectors investing in AI
➢Law
➢Marketing and Advertising
➢Finance
➢Retail and Customer Service
➢Healthcare
https://www.raconteur.net/technology/top-5-sectors-using-artificial-intelligence

1. Computing (Edge)
2.Cloud (Computing)
3.Crowd (Computing)
The THREE C’s

Edge over the years!
1969 2019
Early 1900 Now
https://en.wikipedia.org/wiki/Operating_theater
https://www.zmescience.com/science/news-science/smartphone-power-
compared-to-apollo-432/

Cloud is here!
Cloud computing is the on-demand availability of computer system
resources, especially data storage and computing power, without
direct active management by the user. The term is generally used to
describe data centers available to many users over the Internet. Large
clouds, predominant today, often have functions distributed over
multiple locations from central servers.

Crowd adds a new dimension

So the Question is
How is Computing at the Edge
and in the Cloud, merged with
Crowd Computing & AI going to
impact Healthcare / Medicine?

AI + Experts for Individual Patients
with Dr. Zoher Ghogawala, Lahey Hospital & Medical Center

When panel votes
overwhelmingly for a
specific treatment –
there is a pattern that is
being recognized
44
Does Expert Opinion
Matter?
Preliminary data shows that patients who select the
option favored by >80% of experts do better (SF36
PCS) than those who have the opposite treatment
- JNS:Spine (2019)

Some issues about Data
➢“Data is the new oil” — Clive Humby (2006)
➢“Data is a valuable, powerful commodity — but
unlike oil, it is unlimited in quantity and in its
capacity for harm” -- James Bridle (2018).
➢“Not everything that counts can be counted, and
not everything that can be counted counts.” —
Einstein
➢Getting fairness in Data Collection Remains a
Challenge (e.g., Face Recognition!)

Errors Computers and Humans Make
➢Computer (auto-correct)
○ Irfan -> Organ, Urban
➢Humans
○ Irfan -> Ifran

Other efforts in Health/Medical Area
➢Aware Home (A Smart Home to
sense residents health related
activities to report/support)
➢Surgical Simulation and Training
➢Gait and Movement Analysis
➢Healthy Eating
➢Medical Imaging
➢see irfanessa.com

“We tend to overestimate the effect
of a technology in the short run
and underestimate the effect in the
long run”
-- Amara's law

Data-Driven
Innovation
Irfan Essa
Technology
irfan@gatech.edu

Data-Driven Innovation and AI in Healthcare

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