This document discusses the potential of artificial intelligence (AI) and emerging technologies in healthcare. It begins with brief introductions and then outlines several key AI use cases in healthcare, including data collection and management, personal health data management, diagnosis, patient management, and macro health analysis. It also discusses challenges like skilled labor shortages and lack of large data sets. Risks of AI like bias, privacy issues, and dangerous mistakes are presented. The conclusion is that AI has great potential to transform healthcare if applications are handled carefully and data is managed appropriately.

1. Healthcare + AI ,
Blockchain & Emerging
Technologies
Srinath Perera, Ph.D.
VP Research WSO2, Apache Member,
( srinath@wso2.com)
@srinath_perera

2. Myself
• I am computer Scientist and knows little about
Healthcare ( I might not use the right Jargon, please
bear with me)
• Although I work in the Industry, I have been an
active researcher ( 1000+ citations)
• I have been an open-source developer (10+ years)
help design a few well-known packages ( e.g.,
Apache Axis2, WSO2 Siddhi)
• I have designed many real-life systems that run by
many fortune 500 companies and governments
2

3. 3
• Some tasks (e.g., make a coffee) we
can explain, precisely. We can write
a code to do them.
• Other tasks, e.g., drive a car, we can
do, but can’t explain how. We can’t
teach our children how to drive by
writing it down. Such “intelligent
tasks,” we have to give them
examples and feedback and train
them.
• AI can learn from examples to do
such “intelligent tasks.”
• Andrew Ng’s rule - “AI can do
anything human can do in 10 sec or
less.”
What is AI?

4. What is the big deal?
• Compared to humans, Computer
doing these tasks (With AI) are
• Faster (1K-1000K times)
• Cheaper to replicate
• Reliable
• AI ability to learning can be used to
extrapolate the future
• Outcomes
• Cheaper, efficient, new
possibilities4

5. AI Use Cases in Healthcare

6. Data Collection and Management
6
• Everything starts with data
• Needs processes and systems to
collect data and manage them.
• Data need to be collected by non-
technical people
• We need to make data collection as
possible, e.g., Collect data like
images, then using AI to extract
information
• Data need to be managed, protected
and shared across organizations

7. Personal Health Data (History) Management
7
• Potential Scenario
• Scan and keep all your health
records ( in the phone, encrypted
and backed up in the Cloud)
• Walked to the doctor
• Scans his App
• Share your data for a limited time
• For full security, we will need
blockchain

8. 8
Diagnosis
• IBM Watson & Google Deepmind Health,
diagnosis as a subscription?
• Build on all data, unto data, learning from
every mistake
• AI has beaten humans in many cases (e.g.,
Kaggle diabetic retinopathy challenge)
• In focused specific disease diagnosis likely
AI will better ( and hard to beat)
• Humans have a chance in broad diagnosis
• Patents built to solve diagnosis problems
can be very valuable
• Our access to data is a significant advantage

9. Patients Management
9
• Using AI to manage the patient through
his stay ( or through a long time)
• AI Use cases
• Risk assessment
• Anomaly Detection (WSO2 Use case)
• Keep them Healthy
• Proactive Tests/ Preventive Actions
& Life cycle changes
• Provide connect more/ relevant
information for diagnosis
• Optimal resource management

10. Macro Health Analysis and Management
10
• Using AI & Analytics to
understand broad health
patterns and act
proactively or reactively
prevent, manage, and
contain situations
• e.g., Dengue hotbed
identification and acting
proactively
• Similar work in other fields - https://lirneasia.net/big-data
Photo Credit, https://lirneasia.net/wp-content/uploads/2019/02/LIRNEasia-Data-Driven-Policy.pdf

11. Challenges
11
• Shortage of Skilled Professionals
• The data scientists, programmers, and architects are
in short supply and expensive (300-500K year).
• It is hard for medium and smaller organizations to
attract and hire enough skilled people.
• Most AI solutions will be delivered as cloud APIs
• Only possible when If data formats are well known
(e.g., banking or healthcare data) and key
performance indicators (KPIs) are well defined. Then
reusable models can be built for those use cases.
• Examples are disease detection in healthcare,
marketing insights, spatiotemporal models, and
analytics for specific sports.

12. Challenges (contd.)
• Lack of Large Enough Data Sets.
• With 10,000 data points per day, it takes
3 years to collect 1 million data points
• Labeled data sets are hard to find
• Interpretability
• Some models are black boxes
• Tracking provenance of data used

13. AI mistakes are Dangerous
13
• AI weaknesses can cause much more
potential harm than the same weakness
in humans
• Broad integration - AI will be everywhere
(e.g., selecting people for an interview,
picking suspicious people for further
investigation).
• Wide application - because the cost to
replicate AI operations is small, AI is applied
on a broader scale than humans
• Transparent application without oversight -
diversity of views among humans detect and
control flowed applications, while AI will be
applied transparently avoid such oversight.
CC https://www.maxpixel.net/House-Of-Cards-Fragile-Sensitive-
Statics-Patience-763246

14. Risks
• Bias
• AI can learn and repeat the inherent bias in
the data caused by human behavior (e.g.,
Book “Weapons of Math Destruction” by Cathy
O’Neil.)
• Removing bias is hard - For example, an
address in a certain neighborhood might act
as a proxy for the race; the name might act as
a proxy for gender, or name of the degree
might act as a proxy for age.
• Privacy: AI can infer sensitive data from
seemingly harmless information
• E.g., detect where you live using
accelerometer data
14

15. 15
Conclusion
• Unlike many hot things, I think AI is here to stay.
• It enables many use cases and even potential to
redefine health care
• Applications are non-trivial and need careful
handling
• With a strong public health system, we have a
real potential to manage data carefully and even
convert it to a competitive advantage
• AI can be a companion, not a competitor