AI in Medicine and Imaging - Stanford Symposium 2020
Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.
health medicine medical-imaging stanford videos computer-vision article video

Advancements of machine learning and artificial intelligence into all areas of medicine are now a reality and they hold the potential to transform healthcare and open up a world of incredible promise for everyone. Sponsored by the Stanford Center for Artificial Intelligence in Medicine and Imaging, the 2020 AIMI Symposium is a virtual conference convening experts from Stanford and beyond to advance the field of AI in medicine and imaging. This conference will cover everything from a survey of the latest machine learning approaches, many use cases in depth, unique metrics to healthcare, important challenges and pitfalls, and best practices for designing building and evaluating machine learning in healthcare applications.

Our goal is to make the best science accessible to a broad audience of academic, clinical, and industry attendees. Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.

Topic covered include:

  • Democratizing Healthcare with AI
  • Regulatory Considerations for AI in Healthcare
  • Technical Advancements in Clinical ML - What's New in 2020
  • Fairness in Clinical Machine Learning
  • Bridging Innovation to Application and more!

Don't forget to tag @stanfordmlgroup in your comment, otherwise they may not be notified.

Authors community post
Our mission is to significantly improve people's lives through our work in AI
Share this project
Similar projects
ML Foundations and Methods for Precision Medicine and Healthcare
This tutorial will discuss ideas from machine learning that enable personalization (useful for applications in education, retail, medicine and recsys).
Sparkle: Combating Medication Non-adherence with ML
Introducing Sparkle ✨: a multi-platform medication monitoring system designed to promote medication adherence for ordinary people.
Reliable Decision Support using Counterfactual Models
We propose using a different learning objective that predicts counterfactuals instead of predicting outcomes under an existing action policy as in ...
Clinical BERT
Repository for Publicly Available Clinical BERT Embeddings
Top collections