As databases of medical information are growing, the cost of analyzing data is falling, and computer scientists, engineers, and investment are flooding into the field, AI in healthcare is subject to increasingly hyperbolic claims. Every week brings news of advances: superior algorithms that can predict clinical events and disease trajectory, classify images better than humans, translate clinical texts, and generate sensational discoveries around new risk factors and treatment effects. Yet the excitement about AI poses risks for its robustness: we must take steps to avoid a reproducibility of the kind that has engulfed other areas of biomedicine and human science in the last decade and shaken public confidence in the validity of scientific work.

1. Leo Anthony Celi MD MS MPH
Massachusetts Institute of Technology
Harvard Medical School
Rise of the Machines:
The Challenges of Medical AI

2. Funding Disclosure
• National Institute of Health
• Philips
• Google
• IBM

3. MIT Laboratory for
Computational Physiology (LCP)

4. Crowdsourcing Medical Knowledge
Discovery and Validation
Medical Information Mart for Intensive Care
Beth Israel Deaconess Medical Center

5. Crowdsourcing Medical Knowledge
Discovery and Validation

7. >20 Health Datathons in 5 Continents

10. Healthcare:
Failed Business Model

11. Opportunities for AI in
Healthcare
Classification

12. Opportunities for AI in
Healthcare
Prediction

13. Opportunities for AI in
Healthcare
Optimisation

14. Challenges for AI in Healthcare

15. Labeling: The Elusive Consensus

16. Professor Michael I. Jordan
Electrical Engineering, Computer Science and Statistics
University of California Berkeley
Model Validity:
Space and Time Limitation

18. Machine Bias Scenarios in
Healthcare

20. The Case for Open Data and
Transparent Algorithms

21. Take Home Messages