Tuesday, January 26, 12 PM, EST

The COVID-19 pandemic poses a challenge for machine learning (ML) algorithms trained on pre-COVID data. Clinical presentations, disease trajectories, radiographic interpretations and recommended medications for COVID all represent a significant departure from previous real-world practice patterns and may decrease the performance of established ML algorithms. 

In this webinar, Zak Kohane, MD, PhD, will discuss this COVID discontinuity in the context of a longstanding issue in the field of AI and machine learning: how can algorithms (and the human physicians interacting with them) adapt to shifts in medical knowledge, whether from a novel therapy or a newly described disease? He will outline potential opportunities for AI researchers, and steps that AI experts and clinicians should take to optimize system performance and patient care.