In this talk, Building up from very basics of AI, I have focused mainly on applications and how AI can help to augment health care delivery.

1. Artificial Intelligence: Connecting the dots
and relieving the pain in Health Care Delivery
JAI NAHAR, MD, MBA
Associate Professor of Pediatrics
George Washington University School of Medicine
Attending, Division of Cardiology
Children’s National Hospital, Washington DC
Jan.25th 2020
SAIMS
Indore, India

2. 1. Introductory concepts
2. History & Current State of AI
3. Potential uses in Augmenting Heath care delivery
4. Challenges in Adoption
5. Future Directions
Agenda

3. Why Talk about AI In Health Care?
• Fourth Industrial Revolution
• Digitized world
• Health Care Delivery Challenges
 Resources
 Access
 Education
 Care Gaps
 Friction

4. Environmental Scan

5. AI : Definition
“Artificial Intelligence is the science of making Machines do
things that would require intelligence if done by men.”
Marvin Minsky

6. AI: General Concepts
Artificial
Intelligence AI
Machine
Learning
ML
Deep Learning
DL
AI: Engineering of making Intelligent machines and programs
ML: Ability of machines to learn without being explicitly programmed
DL: Learning based on Deep Neural Networks

7. Artificial Intelligence in Medicine

8. Types of AI
Slide obtained with permission from: Chang, AC

9. Machine Learning types
1. Supervised
2. Unsupervised
3. Reinforcement

10. Supervised Learning
• Goal is to predict a known output orTarget
• Algorithm is taught with right answers (labels) for examples used in
training data set.
• Can be categorized as:
 Classification problem: predicting categories, discrete value output
(0,1 etc.)
 Regression problem: predict continuous values
 Anomaly detection problem: predict unusual pattern

11. Unsupervised Learning
• Goal is to learn the intrinsic structure within data.
• No outputs to predict
• Task is to find hidden pattern/structure in data,
without human feedback
Cluster2
Cluster3
Cluster
1

12. Reinforcement learning
Reinforcement learning led to AlphaGo’s stunning victory over a human Go
champion
https://www.technologyreview.com/s/603501/10-breakthrough-technologies-2017-reinforcement-
learning/
Reinforcement learning is
learning by trial-and-error,
solely from rewards or
punishments.
https://deepmind.com/blog/deep-reinforcement-
learning/
Can be viewed as hybrid
of supervised and
unsupervised learning

13. Artificial Neural Network (ANN)
ANN are modeled after human neurons
• Nodes are like neurons
• Input layer: input data/ predictor variables/ features
• Hidden layer: processing of input
• Output layer:Target (prediction of class or value)
Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep Learning in Medical Imaging: General
Overview. Korean Journal of Radiology, 18(4), 570–584. http://doi.org/10.3348/kjr.2017.18.4.570

14. Deep Learning
• Part of Machine Learning
• Uses multiple layers of ANN
• Mimics the working of human brain
Lee, J.-G., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep Learning in Medical Imaging: General
Overview. Korean Journal of Radiology, 18(4), 570–584. http://doi.org/10.3348/kjr.2017.18.4.570

15. Deep Learning
Three types of Network:
1. Deep Neural Networks: Google’s Deep mind Alpha Go
2. Recurrent Neural Networks (RNN): natural language
processing, handwriting and speech recognition
3. Convolutional Neural Networks (CNN): Computer
vision, image recognition, CV imaging

16. Cognitive Computing
Systems mimicking human cognition, help
replicate human capabilities across the spectrum of sensory
perception, deduction, reasoning, learning and knowledge.

17. Physician’s Brain and Machine-Equivalent capabilities
Chang A. Analytics and Algorithms, Big Data, Cognitive Computing, and Deep Learning in Medicine and Health Care. AI Med Ebook; 2017

18. 1. Introductory concepts
2. History & Current State of AI
3. Potential uses in Augmenting Heath care delivery
4. Challenges in Adoption
5. Future Directions
Agenda

19. The synergistic Convergence
A primer in artificialintelligence in cardiovascularmedicine
J. W. Benjamins · T. Hendriks · J. Knuuti · L. E. Juarez-Orozco · P. van der Harst
Neth Heart J (2019) 27:392–402
https://doi.org/10.1007/s12471-019-1286-6

20. What AITechnologies are available for Health Care ?
• ComputerVision
• Natural Language Processing (NLP)
• Conversational AI
• Virtual Assistants
• HybridTechnologies:AR/VR+ AI, Robotics+AI
• Robotic Process Automation (RPA)

21. Current State of AI: Health Care Delivery
1. Automation, CAD, Diagnostics
2. Risk Stratification
3. Prediction, Early Warning System
4. Precision Medicine
5. Home monitoring/Digital Therapeutics
6. Virtual Assistants/Conversational agents

22. 1. Introductory concepts
2. History & Current State of AI
3. Potential uses in Augmenting Heath care delivery
4. Challenges in Adoption
5. Future Directions
Agenda

23. Automation, CAD, Diagnostics
• Radiological diagnosis
• Diabetic Retinopathy
• Prenatal/Fetal Monitoring
• Arrhythmia Detection
Increase in
Access
Targeted
Intervention
Automation
Decreases
Latency in
Diagnosis

24. Radiology: Computer Aided Detection
Critical Care
Suite: UCSF and
GE collaboration.
AI embedded in
Mobile X ray unit

25. AI aided TB screening
http://blog.qure.ai/notes/scaling-up-tb-
screening-with-ai

26. TB: Prompt diagnosis and treatment
http://blog.qure.ai/notes/scaling-up-
tb-screening-with-ai

27. Diabetic retinopathy screening
IDx- DR
First FDA approved AI algorithm
for the detection of DR in the offices of
non-ophthalmic healthcare practitioners
Padhy SK, Takkar B, Chawla R, Kumar A. Artificial intelligence in diabetic
retinopathy: A natural step to the future. Indian J Ophthalmol [serial
online] 2019 [cited 2020 Jan 24];67:1004-9. Available
from: http://www.ijo.in/text.asp?2019/67/7/1004/261034
https://www.eyediagnosis.co/

28. IDx- DR
https://www.eyediagnosis.co/

29. Prenatal Maternal–Fetal Monitoring
https://economictimes.indiatimes.com/small-biz/startups/features/bill-melinda-
gates-foundation-backed-janitri-is-preventing-newborn-deaths-in-rural-
india/articleshow/72394188.cms?from=mdr

30. Intrapartum Maternal–Fetal Monitoring
Janitri
Keyar and Daksh: Intelligent Labor Monitoring solution
https://www.thebetterindia.com/202538/bengal
uru-startup-medical-tech-solution-pregnancy-
maternal-mortality-rate-india/
https://economictimes.indiatimes.com/small-
biz/startups/features/bill-melinda-gates-foundation-
backed-janitri-is-preventing-newborn-deaths-in-rural-
india/articleshow/72394188.cms

31. Arrhythmia Detection
https://directorsblog.nih.gov/2019/01/15/using-artificial-
intelligence-to-catch-irregular-heartbeats/

32. Arrhythmia Detection using AI
Cardiologist-level arrhythmia detection and classification in
ambulatory electrocardiograms using a deep neural network
Hannun,A.Y., Rajpurkar, P., Haghpanahi, M. et al. Cardiologist-level
arrhythmia detection and classification in ambulatory
electrocardiograms using a deep neural network. Nat Med 25, 65–69
(2019) doi:10.1038/s41591-018-0268-3
https://www.ncbi.nlm.nih.gov/pubmed/30617320

33. Arrhythmia Detection using AI
https://stanfordmlgroup.github.io/projects/ecg/

34. Prediction, EarlyWarning System

35. Precision Medicine

36. Making the Invisible,Visible
Known
Unknown

37. Phenomapping
Use of unsupervised machine learning in Phenotypic
classification of a heterogeneous clinical syndrome into discrete
phenogroups
PG1
PG2
PG3
PG4
PG5
Phenomapping

38. Deep Phenotyping
Detect Unknown High
risk groups
Develop refined High risk
stratification models
Target timely
prevention/Intervention
Deep Phenotyping

39. Dawn of New Era of
Augmented Intelligence
Physician and AI ( Human/Machine) synergy for facilitating better
• Diagnosis
• Clinical Decisions
• Disease management
Human
Machine
Intelligence
Augmented
Platform for
Precision Medicine

40. AI augmented remote Patient monitoring
Traditional
Hospital
ER
OPD
Home/
Natural
environment
Life Style,
Behavior
SDOH

41. Home monitoring
Smart remote devices
https://hbr.org/2019/05/the-health-care-benefits-of-combining-wearables-and-ai

42. Remote patient Monitoring
Remote
Monitoring
Wearable
Sensors
Smart
Devices
Virtual
assistants
Implanted
devices/sensors
Digital Biomarkers:
• Vital signs
• EKG
• Blood glucose
• Sleep
• Emotional state
• Activity
• Movements
Voice biomarkers

43. AI powered remote Patient monitoring
Augmented
health care
Remote
patient
monitoring
Real time
analytics and
AI
Telemedicine
Virtual
Assistants

44. Virtual Assistants/Conversational AI
Hello!
How can I help
you?

45. History: Human Computer Interaction
GUI
WEB
www
Mobile
VUI
1980s 1990s 2000s Present
Voice:
• Natural mode of
communication
• Convenient
• Scalable
• Rich Content
• Metadata

46. Voice Augmented World
Health
Care
AIVoice
Health Care
Conversational
AI

47. Conversational AI
Conversational AI: Technology which allows Human Machine interaction
through the use of Natural conversation, utilizing voice user interface and
Machine Intelligence.
Human Machine
Conversational AI
Voice
Technology
NLP
ML,
Deep
Learning
Synergistic
Convergence
Conversation

48. Intelligent Virtual Agents
Task Execution using Machine intelligence
1
Perception/Sense
2
Understanding
Comprehension
3
Action/Execution
Speech/Voice /Image
Recognition
Natural Language
processing
Computer Vision
Natural
Language
Generation
4
Learn from past
actions/experiences
Machine
Learning

49. Current State
Conversational AI Ecosystem
Smart
Speakers
Smart
Displays
Smart voice
enabled
devices
Chatbots
Virtual
Assistants
Ambient
Clinical
Intelligence
Conversational
AI
Voice
VUI
AI
Ambient
Sensors

50. Conversational
AI
Outpatient
clinic
Home
On the Go
In Hospital
Conversational AI touchpoints in Health Care
Delivery
1. EHR documentation, navigation
2. Clinical Decision Support
3. Foreign Language Interpretation
1. Appointment Navigation
2. Patient Education
3. Patient Engagement
4. Medication management
5. Chronic care management:
bridging the care gap
1. Mobile health units/EMS
2. Patient Education
3. Care Continuum
4. Access to care providers
5. Access to Information
1. Patient Education, inpatient care
navigation
2. Discharge preparation
3. Clinical Decision Support
4. Operating suites: Hands free clinical
documentation and information retrieval
Decreasing Physician
Burnout: POC Tasks
Home Health
Optimization
Easing the Hospital Journey
Bridging the Care
Gap

51. Livongo for Diabetes program
HIPAA complaint Alexa Health care skill
• Right information
• Right time
• Guides behavior and rational decision making
https://www.youtube.com/watch?v=ShCPsCcy_TM
Task: Information exchange,
behavior modification,
Rational decision making
Technology: Alexa
Environment: Patient’s home

52. 1. Introductory concepts
2. History & Current State of AI
3. Potential uses in Augmenting Heath care delivery
4. Challenges in Adoption
5. Future Directions
Agenda

53. What are the Challenges in AI Adoption?
• Privacy
• Security
• Accuracy
• Reliability
• Explainability
• Trust
• Ethics
• Design and development

54. 1. Introductory concepts
2. History & Current State of AI
3. Potential uses in Augmenting Heath care delivery
4. Challenges in Adoption
5. Future Directions
Agenda

55. Future directions
• AI Education
• Multi stakeholder collaboration
• Sharing of best practices
• Ethical use and Societal Good

56. ClosingThoughts
Man + Machine synergy = Strengthens the power of healing
Team effort: Collaboration of all stakeholders with shared vision and aligned
interests is key to excel.

57. Thanks for your time!

58. Educational Resources
AI Med
https://ai-med.io/