Talk given with Anitha Kannan at MLConf 2019. Link to abstract here: https://mlconf.com/speakers/anitha-kannan/

1. AI for healthcare: Scaling access and
quality of care for everyone
Anitha Kannan
Xavier Amatriain
MLConf 10/08/2019

2. ● >50% world’s population with
no access to essential health
services
● US…
○ 10% of adult population
has no health insurance
○ 28% of working adults are
under insured
Healthcare access is a major issue
Kaiser Family Foundation analysis of the 2017 National Health Interview Survey
Merrit Hawkins, 2017 survey
shortage of 120,000 physicians by 2030

3. Patient-Doctor interaction
● Doctors have ~15 minutes to capture
pertinent information about a patient,
diagnose + recommend treatment
● 30% of the medical errors causing
~400k deaths a year are due to
misdiagnosis
2069 doctors solve 1572 HumanDx cases

4. Online search and/or Healthcare access?
“72% of internet users say
they looked online for
health information within
the past year”
“More than ⅓ use Internet
to self-diagnose”
[Pew Research]
1.4M daily
25M daily
Need more than Google can
deliver
Less cost and friction
than PCP visit

5. We have an opportunity to
reimagine healthcare

6. We have an obligation
opportunity to reimagine
healthcare

7. Looking Forward: Towards AI powered Learning Health Systems
● AI + human practitioners
for Quality Care
● Less than 20% of the cost
for best healthcare access
● Mobile First Care, 24/7
always on

8. What are we doing?
● Mission: Provide the world's
best healthcare for everyone
● Product: User-facing mobile
primary care app
● Team: Building an awesome
and diverse team
● Approach: State-of-the-art
AI/ML + product/UX/clinical
AI-based interaction
AI + Health coaches
AI + Doctors

9. Breakthroughs in AI & healthcare

10. Peer-reviewed research at Curai

11. AI “in the wild”: Learning health systems

12. Automation/AIAutomation/AI
Automation/AI in healthcare
Pertinent
information
gathering
Assessment
(Diagnosis,
Triaging etc)
Plan
(Next steps,
treatments)
Chief
complaints

13. AI in the wild: Desired properties
● Easily extensible
○ Incrementally/iteratively learn from
“physician-in-the-loop” or from
additional data
● Knows what it does not know
○ Models uncertainty in prediction
○ Enables fall-back to
“physician-in-the-loop”

14. Automation/AI in healthcare
Automation/AIAutomation/AI
Pertinent
information
gathering
Assessment
(Diagnosis,
Triaging etc)
Plan
(Next steps,
treatments)
Chief
complaints

15. AI for assisted diagnosis (since 1980s)
● Expert systems
○ Mycin, Internist-1, DxPlain, VDDx,
QMR
● Covers over 1000 diseases
and 3500+ findings
○ Most comprehensive diagnosis
model, so far
○ 30+ years of expert curation
based on research and
evidence-based literature

16. Expert systems in the wild?
● Not easy to extend
○ Costly, time consuming and
time-delayed
○ Poor generalization to new places
● Does not know what “it
doesn’t know”
○ Constrained to diseases in the
system

17. Assisted diagnosis in the wild
1. Extensibility
a. Diagnosis as a ML task
i. Expert systems as a prior
b. Modeling less prevalent diseases
i. Low-shot learning
2. Knowing what you don’t know
a. Measures of uncertainty in prediction
b. Allows fall-back to
“physician-in-the-loop”

18. Assisted diagnosis in the wild
1. Extensibility
a. Diagnosis as a ML task
i. Expert systems as a prior
b. Modeling less prevalent diseases
i. Low-shot learning
2. Knowing what you don’t know
a. Measures of uncertainty in prediction
b. Allows fall-back to
“physician-in-the-loop”

19. x
Clinical case simulator
Example of simulated case
Knowledge base
central to expert
systems
Expert systems as prior

20. ML models for diagnosis
clinical cases simulated
from expert system
From expert systems to ML model for diagnosis

21. ML models for diagnosis
clinical cases simulated
from expert system
From expert systems to ML model for diagnosis
clinical cases from other sources eg.
electronic health records

22. Assisted diagnosis in the wild
1. Extensibility
a. Diagnosis as a ML task
i. Expert systems as a prior
b. Modeling less prevalent diseases
i. Low-shot learning
2. Knowing what you don’t know
a. Measures of uncertainty in prediction
b. Allows fall-back to
“physician-in-the-loop”

23. Assisted diagnosis in the wild
1. Extensibility
a. Diagnosis as a ML task
i. Expert systems as a prior
b. Modeling less prevalent diseases
i. Low-shot learning
2. Knowing what you don’t know
a. Measures of uncertainty in prediction
b. Allows fall-back to
“physician-in-the-loop”

24. Open-set diagnosis
Amblyopia
Gastroenteritis
Diseases within
diagnostic scope

25. Open-Set diagnosis
Universe of diseases
Amblyopia
Diabetic
Ophthalmoplegia
Gastroenteritis
is aware and avoid misclassifying unknown diseases as known
Diseases within
diagnostic scope

26. Open-Set diagnosis
Universe of diseases
Amblyopia
Diabetic
Ophthalmoplegia
Extra diseases
Gastroenteritis
avoids
misclassifying
unknown
diseases as
known.
Diseases within
diagnostic scope
Entropic open-set loss: Maximize predictive
entropy of unseen examples

27. AI in-the-wild: Desired properties
● Easily extensible
○ Incrementally/iteratively learn from
“physician-in-the-loop” or from
additional data
● Knows what it does not know
○ Models uncertainty in prediction
○ Enables fall-back to
“physician-in-the-loop”

28. Medical Information gathering “in-the-wild”
Real users with health
issues that an AI medical
agent may not understand

29. Looking Forward...
● AI + human practitioners
for Quality Care
● Less than 20% of the cost
for best healthcare access
● Mobile First Care, 24/7
always on
AI-based interaction
AI + Health coaches
AI + Doctors
https://firstopinionapp.com/
39.6 M Californians
with access to high
quality affordable
primary care