AI for Health Treatments by Philippe Gerwill

Philippe Gerwill's Keynote about "AI for Health Treatments at the Afya Summit in Sao Paulo on 28/08/2024

1. AI for Health
Treatments
Author: Philippe GERWILL

2. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

3. •French citizen
•Over 30 Years of experience in global
corporations like Ciba or Novartis
•Digital health influencer and futurist
•TEDx and International keynote speaker
•Topics: digital health, healthcare in the
metaverse or AI in healthcare
•Executive advisor and board member for
various startups and tech/healthcare
companies
•Mentor/coach
Philippe Gerwill

4. •Population: 217 million
7th most populous country
•Surface: 8.51 million km2
5th largest country in the World
•Healthcare spending:
1’250 USD per capita
•Number of doctors:
584k in 2022
163k in Sao Paulo State
1k in Roraima State
2.41 doctors per 1’000 people
Key Numbers for Brazil
Healthcare

5. Healthcare
Challenges in Brazil
•Distance to travel to visit:
A doctor: 72km in average
A specialist: 155km in average
•Number of hospitals:
About 7’000
•Hospital beds density:
In 2010: 2.23 per 1’000 people
In 2020: 1.91 per 1’000 people
•Main causes of death:
Heart and cerebrovascular diseases
Cancers (mainly lung and breasts)

6. •Biggest health issue:
Mental health according to recent survey
•Increasing obesity:
In 2008:
45% overweight
14% obese
In 2023:
61% overweight
24% obese
•Rising costs of overweight
In 2019: 37 billion USD
In 2030: 60 billion USD
In 2060: 218 billion USD
Healthcare Challenges
in Brazil

7. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

8. Overview of AI
and Relevance
in Healthcare
•Reduce administrative
burden to free up capacity
for value-added tasks
•Leverage AI to uncover
insights and correlations from
vast data sets beyond human
processing capabilities

9. •Automation of Routine Tasks:
Administrative Processes:
Streamlined scheduling, billing, and data entry
Patient Management:
AI-driven chatbots and virtual assistants
•Resource Optimization:
Predictive Analytics:
Efficient staff and bed management
Supply Chain Management:
Optimized inventory control
AI in operational
efficiency –1/3

10. AI in operational
efficiency –2/3
•Workflow Streamlining:
Clinical Decision Support:
Real-time data integration
Interdepartmental Coordination:
Improved communication
•Cost Reduction:
Operational Savings:
Automation-driven cost efficiencies
Reduced Length of Stay:
Optimized treatment pathways

11. AI in operational
efficiency –3/3
•Improved Patient Experience:
Reduced Wait Times:
Prioritized scheduling and workflows
Enhanced Personalization:
Tailored patient interactions

12. •Enhanced Diagnostic Accuracy:
Medical Imaging:
AI identifies anomalies in X-rays, MRIs, CT scans
Pattern Recognition:
Early detection of cancer, cardiovascular diseases
•Faster Diagnosis:
Real-Time Analysis:
Instant insights into diagnostic data
Automated Reporting:
Streamlined report generation
AI in Diagnostics and
Treatment –1/3

13. •Personalized Treatment Plans:
Data-Driven Insights:
Tailored treatments based on health profiles
Predictive Analytics:
Anticipating patient responses
•Integration with EHRs:
Data Aggregation:
Comprehensive patient health views
Decision Support Systems:
Enhanced clinical decision-making
AI in Diagnostics and
Treatment –2/3

14. AI in Diagnostics
and Treatment –3/3
•Improved Patient Outcomes:
Reduced Errors:
Higher accuracy in diagnostics and
treatments
Proactive Care:
Early intervention for at-risk
patients

15. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

16. Healthcare Metaverse
•Fitness & wellbeing
•Virtual gym & sports
•Virtual relaxation
•Telemedicine
•Mental health
•Medical training & education
•Etc…

17. Fitness & Wellbeing
•Health Care vs Sick Care
•Preventative care
•Nudging
•Tokenization

18. Virtual Gym & Sports
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19. Virtual Relaxation

20. Telemedicine
REMOTE CARE SHIFT OF THE
POINT OF CARE
VIRTUAL
HOSPITALS
MEDICAL
DIAGNOSIS
VIRTUAL
THERAPIES

21. Mental Health
PTSD Anxiety
disorders
Delusions Psychosis Hallucinations Eating
disorders
Etc…

22. Medical Training & Education
Get trained on a simulation platform enabled by AR/VR/XR technology, risk free

23. Aimedis Avalon

24. United Arab Emirates

25. Japan

26. Australia

27. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

28. The First Humanized AI
Dedicated to Women’s Health

34. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

35. Genomics, Personalized
Medicine & AI – 1/4
•Genomics: The Foundation of
Personalized Medicine
Genomic Data:
Understanding DNA and genetic variations
Disease Understanding:
Identifying genetic predispositions

36. Genomics, Personalized
Medicine & AI – 2/4
•Personalized Medicine: Tailoring Treatment to the
Individual
Precision Medicine:
Customized treatment plans
Targeted Therapies:
Specific treatments based on genetic mutations

37. •AI's Role in Genomics and Personalized
Medicine
Data Analysis:
AI interpreting vast genomic datasets
Drug Discovery:
Accelerating the development of new treatments
Predictive Analytics:
Risk assessments and personalized prevention
Genomics, Personalized
Medicine & AI – 3/4

38. Genomics, Personalized
Medicine & AI – 4/4
•Applications and Case Studies
Cardiovascular Disease:
Genetic risk assessment
Oncology:
AI in cancer treatment
Rare Genetic Disorders:
AI-driven diagnosis

40. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

41. •The Shift from Reactive to Proactive
Healthcare
Proactive Care:
AI-driven early detection and prevention
strategies
Predictive Analytics:
Identifying risk profiles and predicting health
outcomes
•Personalized Wellness Programs
Customized Plans:
Tailored health and wellness recommendations
Continuous Monitoring:
Real-time tracking of health metrics
Wellness &
Preventative Care – 1/3

42. •Chronic Disease Management and
Prevention
Personalized Care:
AI in managing and preventing chronic conditions
Preventative Interventions:
Lifestyle changes and early intervention
•Mental Health and Wellness
Mental Health Monitoring:
Early detection of mental health issues
Digital Therapeutics:
AI-powered mental health support
Wellness &
Preventative Care – 2/3

43. •AI-Enabled Preventative Care in Public
Health:
Epidemiology and Outbreak Prediction:
AI predicts disease outbreaks and guides public
health responses by analyzing diverse data
sources
Vaccination Campaigns:
AI helps optimize vaccination campaigns by
identifying populations at risk, predicting vaccine
demand, and ensuring efficient distribution
Wellness &
Preventative Care – 3/3

44. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness & Preventative Care
•Key challenges
•Questions & Answers
Agenda

45. •Data Privacy and Security
The integration of AI in healthcare requires access
to large volumes of sensitive patient data,
including genomic information, medical records,
and real-time health monitoring data
•Bias and Fairness
AI models can inherit biases present in training
data, leading to unfair treatment
recommendations or diagnostics, especially for
underrepresented populations
Key challenges – 1/5

46. Key challenges – 2/5
•Ethical and Legal Considerations
The use of AI in areas like genomics and personalized medicine
raises significant ethical questions, including concerns about
genetic discrimination, informed consent, and the ownership of
genetic data
•Interoperability and Integration
AI systems often need to be integrated with existing healthcare
IT infrastructure, such as Electronic Health Records (EHRs),
which may use different formats, standards, and technologies

47. •Regulatory Approval and Oversight
AI-driven tools, especially in diagnostics and
treatment, must undergo rigorous validation and
obtain regulatory approval (e.g., FDA approval)
before being deployed in clinical settings
•Cost and Accessibility
The development, deployment, and maintenance
of AI technologies in healthcare can be expensive,
potentially limiting access to well-funded
institutions or regions
Key challenges – 3/5

48. •Trust and Acceptance
Healthcare professionals and patients may be
skeptical about relying on AI for critical decisions,
particularly in diagnosis and treatment
•Clinical Validation and Evidence
AI models need extensive clinical validation to
ensure they provide accurate and reliable
outcomes across diverse patient populations
Key challenges – 4/5

49. •Scalability and Generalization
AI models trained on specific datasets or in
specific environments may not generalize well to
other settings, limiting their scalability
•Impact on Workforce and Job Roles
The introduction of AI in healthcare could lead to
concerns about job displacement, particularly
among administrative and support staff
Key challenges – 5/5

50. •Introduction
•Role of AI in Healthcare
•Healthcare Metaverse
•My Anna Health
•Genomics, Personalized Medicine & AI
•Wellness and Preventative Care
•Key challenges
•Questions & Answers
Agenda

51. Questions & Answers