Is There a Post-Pandemic AI Panacea

Overview

Explore the challenges and potential of AI in pandemic response through this academic panel discussion featuring health AI specialists. Examine why AI had limited impact during COVID-19, analyze data science hurdles in healthcare, and uncover lessons learned for future crisis management. Gain insights into the strengths and weaknesses of AI applications in epidemiology, radiology, and healthcare modeling. Delve into critical issues such as data infrastructure, health data poverty, trust, transparency, and regulatory barriers. Discover strategies for training multidisciplinary researchers and integrating AI effectively in healthcare systems. Learn from expert perspectives on AI failures, implementation challenges, and the future potential of AI in addressing global health crises.

Syllabus

Introduction
How is the state of AI deployment
What do radiologists think about AI
How does AI affect radiologists
How can AI be used in healthcare
How can AI be used in modelling
Data infrastructure
Health data poverty
Trust issue
Barriers to implementation
Trust and transparency
How to encourage transparency
The potential of AI
Paul Elliott
DeepMind
Training the next generation of researchers
Developing multidisciplinary people
AI failure
Barriers to AI integration
Regulation
Wrap up