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AI for COVID-19: An online virtual care
approach
X. Amatriain, PhD Geoff Tso, MD Anitha Kannan, PhD
COVID-19 and AI Virtual Conference
Stanford 04/01/2020
AI-powered virtual care
for everyone
โ— >50% world with no access to
essential health services
โ—‹ ~30% of US adults under-insured
โ— ~15 min. to capture information,
diagnose, recommend treatment
โ— โ…“ of Americans self-diagnose
online
Healthcare access and scalability
shortage of 120,000 physicians by 2030
Towards AI powered scalable health systems
โ— Mobile-First Care, always
on, accessible, affordable
โ— AI + human providers in
the loop for quality care
โ— Always-Learning system
โ— AI to operate in-the-wild
(EHR)
How does this look like?
Our approach to COVID-19
Personalized Diagnostic Assessment
AI + Providers in the loop + Daily follow up
Working on...
โ— Added at-home testing
capabilities to our platform,
waiting for FDA to approve
โ— In discussions with existing
health systems who may
use platform to scale and
improve access
Peer-reviewed research at Curai
Question similarity
โ— Transfer learning
โ— Double-finetune BERT model
โ— Handle data sparsity
โ— Medical domain knowledge
through an intermediate QA binary
task
โ— Out-of-the-box applied to
COVID specific Q/A
BERT
pre-trained
weights
BERT
question
Label Loss
answer
BERT
Q
Label Loss
Q
QA
Automated Question/Answering
User Questions
Matching Questions in our FAQ
(Answer not shown here for brevity)
When do COVID symptoms start after exposure? How long is it between when a person is exposed to the
virus and when they start showing symptoms?
I am asymptomatic and have been social
distancing/self-isolating for the past x days. Can I
still transmit the infection?
How can someone pass along coronavirus when
asymptomatic? If not sneezing or coughing, how can they
infect others?
Currently Iโ€™m experiencing a cough and slight
chest pain. Should I just stay at home? At what
point will I know I have to go to the ER?
When should you go to
the emergency room?
โ— Automated question answering via question similarity
โ— Practitionersโ€™ vetted answers with ability to follow up
ML + Expert systems for Dx models
female
middle aged
fever
cough
Influenza 16.9
bacterial pneumonia 16.9
acute sinusitis 10.9
asthma 10.9
common cold 10.9
influenza 0.753
bacterial pneumonia 0.205
asthma 0.017
acute sinusitis 0.008
pulmonary tuberculosis 0.007
Inputs
DDx with expert systems DDx with ML model
Expert
system
Clinical case
simulator
Clinical cases
DDx
ML
model
Common cold
UTI
Acute bronchitis
Female
Middle-aged
Chronic cough
Nasal congestion
Other data
(e.g. EHR)
Feedback loop
Inputs DDx before COVID DDx after COVID
female
middle aged
fever
cough
healthcare worker
influenza 0.753
bacterial pneumonia 0.205
asthma 0.017
acute sinusitis 0.008
pulmonary tuberculosis 0.007
COVID-19 0.913
influenza 0.048
bacterial pneumonia 0.024
pulmonary tuberculosis 0.004
adenovirus infection 0.003
female
middle aged
fever
cough
nasal congestion
influenza 0.634
adenovirus infection 0.159
bacterial pneumonia 0.114
acute sinusitis 0.05
asthma 0.019
influenza 0.512
COVID-19 0.256
adenovirus infection 0.106
bacterial pneumonia 0.069
acute sinusitis 0.026
Conclusions
โ— Healthcare needs to scale quickly, and this has become obvious in a global
pandemic like the one we are facing
โ— The only way to scale healthcare while improving quality and accessibility is
through technology and AI
โ— AI cannot be simply โ€œdroppedโ€ in the middle of old workflows and approaches
โ—‹ It needs to be integrated in end-to-end medical care benefitting both patients and providers

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AI for COVID-19: An online virtual care approach

  • 1. AI for COVID-19: An online virtual care approach X. Amatriain, PhD Geoff Tso, MD Anitha Kannan, PhD COVID-19 and AI Virtual Conference Stanford 04/01/2020
  • 3. โ— >50% world with no access to essential health services โ—‹ ~30% of US adults under-insured โ— ~15 min. to capture information, diagnose, recommend treatment โ— โ…“ of Americans self-diagnose online Healthcare access and scalability shortage of 120,000 physicians by 2030
  • 4. Towards AI powered scalable health systems โ— Mobile-First Care, always on, accessible, affordable โ— AI + human providers in the loop for quality care โ— Always-Learning system โ— AI to operate in-the-wild (EHR)
  • 5. How does this look like?
  • 6. Our approach to COVID-19
  • 8. AI + Providers in the loop + Daily follow up
  • 9. Working on... โ— Added at-home testing capabilities to our platform, waiting for FDA to approve โ— In discussions with existing health systems who may use platform to scale and improve access
  • 11. Question similarity โ— Transfer learning โ— Double-finetune BERT model โ— Handle data sparsity โ— Medical domain knowledge through an intermediate QA binary task โ— Out-of-the-box applied to COVID specific Q/A BERT pre-trained weights BERT question Label Loss answer BERT Q Label Loss Q QA
  • 12. Automated Question/Answering User Questions Matching Questions in our FAQ (Answer not shown here for brevity) When do COVID symptoms start after exposure? How long is it between when a person is exposed to the virus and when they start showing symptoms? I am asymptomatic and have been social distancing/self-isolating for the past x days. Can I still transmit the infection? How can someone pass along coronavirus when asymptomatic? If not sneezing or coughing, how can they infect others? Currently Iโ€™m experiencing a cough and slight chest pain. Should I just stay at home? At what point will I know I have to go to the ER? When should you go to the emergency room? โ— Automated question answering via question similarity โ— Practitionersโ€™ vetted answers with ability to follow up
  • 13. ML + Expert systems for Dx models female middle aged fever cough Influenza 16.9 bacterial pneumonia 16.9 acute sinusitis 10.9 asthma 10.9 common cold 10.9 influenza 0.753 bacterial pneumonia 0.205 asthma 0.017 acute sinusitis 0.008 pulmonary tuberculosis 0.007 Inputs DDx with expert systems DDx with ML model Expert system Clinical case simulator Clinical cases DDx ML model Common cold UTI Acute bronchitis Female Middle-aged Chronic cough Nasal congestion Other data (e.g. EHR)
  • 14. Feedback loop Inputs DDx before COVID DDx after COVID female middle aged fever cough healthcare worker influenza 0.753 bacterial pneumonia 0.205 asthma 0.017 acute sinusitis 0.008 pulmonary tuberculosis 0.007 COVID-19 0.913 influenza 0.048 bacterial pneumonia 0.024 pulmonary tuberculosis 0.004 adenovirus infection 0.003 female middle aged fever cough nasal congestion influenza 0.634 adenovirus infection 0.159 bacterial pneumonia 0.114 acute sinusitis 0.05 asthma 0.019 influenza 0.512 COVID-19 0.256 adenovirus infection 0.106 bacterial pneumonia 0.069 acute sinusitis 0.026
  • 15. Conclusions โ— Healthcare needs to scale quickly, and this has become obvious in a global pandemic like the one we are facing โ— The only way to scale healthcare while improving quality and accessibility is through technology and AI โ— AI cannot be simply โ€œdroppedโ€ in the middle of old workflows and approaches โ—‹ It needs to be integrated in end-to-end medical care benefitting both patients and providers