The catastrophe of traditional statistical learning

Deep learning has seen slower adoption in the medical field than in other areas. One reason for that is, perhaps counterintuitively, the prevalence of predictive modeling in medicine. Many people work on clinical prediction models—and most of those people cut their teeth in the era when small specialized models were the norm. The journey from small specialized models to big general models has challenged age-old concepts in machine learning. Traditional notions—such as the importance of interpretability, the risk of overfitting with more parameters, and the necessity of task-specific training data—have been replaced with unconventional insights. More parameters often lead to better generalization, individual parameter interpretation has lost its emphasis, and big models can be trained for multiple tasks, unlocking new capabilities.

In this Endpoints webinar, join Charles Fisher, Founder, and CEO of Unlearn.AI, to unlearn old intuitions and discover how we can apply these insights to navigate the next frontier in machine learning and healthcare. This talk is for anyone interested in understanding why machine learning techniques have advanced so significantly in the last 15 years, why the machine learning revolution is here to stay, and how these changes will impact the future of medicine.

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Charles Fisher

Charles Fisher

CEO, Unlearn.AI

Dr. Charles Fisher is CEO of Unlearn.AI, a role he’s held since co-founding the company in 2017 with the aim of advancing artificial intelligence to eliminate trial and error in medicine. Unlearn develops generative artificial intelligence technologies to create digital twins of individual patients that forecast their future health outcomes and leverages these AI models to accelerate clinical research. Prior to Unlearn, Dr. Fisher was a theoretical physicist and software engineer working at the intersection of machine learning and biology. In industry, he worked as a machine learning engineer at virtual reality startup Leap Motion and as a computational biologist at Pfizer, where he developed machine learning-based approaches to the analysis of large-scale ‘omics data. As an academic researcher, Dr. Fisher was a Phillippe Meyere Fellow in theoretical physics at École Normale Supérieure in Paris, France, and a postdoctoral scientist in theoretical biophysics at Boston University. He holds a Ph.D. in biophysics from Harvard University and a B.S. in biophysics from the University of Michigan.

Kari Abitbol

Kari Abitbol

Director, Client Success, Endpoints News

As head of the client success team at Endpoints News, Kari oversees campaign delivery and strategy for all advertising and client-directed webinars. She brings nearly 20 years of diverse experience across strategic communications, content development and operational leadership.