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 Harvard Medical School Executive Education 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.
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