Building Better Medical AI for Clinical Deployment at Scale
In recent years, we have seem several research breakthroughs demonstrating the potential of AI in healthcare settings. However, we are yet to see AI have any impact in the real world and improve patient outcomes. In this discussion, I will lay out some of the key challenges of developing and deploy AI at scale in clinical settings and introduce some of my work done at Google towards addressing them. We will then have an open discussion on how we can accelerate the solving of these challenges and realize the potential of AI in clinical settings.
*Why AI is yet to have real world patient impact? what are the key technical and non-technical challenges we need to address for this to happen?
*How we can address those challenges systematically? I will be drawing upon examples from my work at Google to illustrate this
*We have all the key ingredients to address these issues and if we can make systematic progress, we can very soon realize patient impact at scale with AI
Vivek is currently working at the intersection of Artificial Intelligence and Healthcare at Google. His work aims at accelerating the translation of state of the art AI/ML to health products and real-world clinical impact. His current research spans improving accuracy, data efficiency, robustness, generalization, fairness, privacy and safety of AI models in healthcare with applications in dermatology, mammography and radiology.
Previously, I worked on Artificial Intelligence based Assistant systems at Facebook improving their ability to understand multimodal data like images, text and speech and interact better with users at scale.