Practical AI Applications in Healthcare From the Ground Up
Mozziyar Etemadi, MD, PhD
Assistant Professor, Anesthesiology, Northwestern Feinberg School of Medicine
Learning Objectives:
- Explain at a high level how a clinical AI system is built
- Identify 2-3 different metrics for how AI systems are evaluated by health systems
- Understand how a physics relationship with unknown constants can be approximated by an AI system
This webinar is the twelfth of a 12-part series that will allow you to claim up to 12 AMA PRA Category 1 Credits™. This webinar will offer 1.0 AMA PRA Category 1 Credits™
Biography:
Dr. Mozziyar Etemadi grew up in the Chicago area and fell in love with technology at an early age, first making websites for local businesses, then working for a start-up internet service provider that used wireless radios to provide broadband to areas that were still on dial-up. He then spent 12 years in the San Francisco Bay Area, training in electrical engineering and finishing medical school. During this time he found several opportunities to bring state-of-the-art technology to solve an actionable, clinical need and was named Forbes “30 Under 30” Scientists. He returned to Chicago because a unique opportunity presented itself at Northwestern Medicine – the flexibility to put top notch engineers inside the clinical environment. Mozzi’s research team at Northwestern started inside one of the intensive care units (ICUs), with engineers working literally feet away from patient rooms. Together with the nurses, physicians, and other staff of the ICU, his team continued to use advanced technologies to solve active problems in clinical workflow, predictive analytics, and wearable physiologic monitoring. Most recently, through a large collaboration with Google Research, Mozzi’s team contributed to a novel, AI-based algorithm that screens patients for lung cancer. Promising early results indicate that this tool can outperform radiologists and can spot cancer sometimes years before the current state of the art.