Iterative Scopes Announces World-Class Inflammatory Bowel Disease (IBD) Focused Scientific Advisory Board

Iterative Scopes, a leading Artificial Intelligence in Gastroenterology provider, announced the expansion of its world class Scientific Advisory Board with experts in the field of Artificial Intelligence (AI) and IBD.

Stan Norton, CTO, noted “Our evolution as an MIT start-up and our continued relationships with Senior Artificial Intelligence Scientists there has allowed us to attract and retain top talent in the very competitive Machine Learning space.  This technical prowess lets us amplify the clinical expertise of the Scientific Advisory Board in ground-breaking ways.”

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Dr. David Rubin, Clinical Chair of the Scientific Advisory Board added, “I’m honored to collaborate with like-minded scientists and physicians who recognize the talented team at Iterative Scopes and the incredible potential to apply machine learning and artificial intelligence to the improved care of patients with chronic digestive illnesses.”

In addition to clinical leaders in IBD, Iterative Scopes is excited to bring on board world-renowned machine learning experts. Polina Golland, Professor at MIT and the Artificial Intelligence Chair of the Scientific Advisory Board at Iterative Scopes adds, “I am excited to work with this impressive team of physicians and the amazing Iterative Scopes machine learning team to bring the advanced machine learning methods to bear on important problems of disease detection and progression modelling, and to ultimately improve patient care and outcomes.”

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Chairs of Iterative Scopes Scientific Advisory Board:

David Rubin, MD (Clinical Chair) – Dr. Rubin is Chief, Section of Gastroenterology, Hepatology and Nutrition, and the Co-Director of the Digestive Diseases Center at The University of Chicago Medicine in Illinois. Dr. Rubin is a world leading researcher on topics involving novel IBD therapies and outcomes, colon cancer prevention, and clinical medical ethics.

Polina Golland, PhD (AI Chair) – Dr. Golland is a Henry Ellis Warren (1894) professor in the Electrical Engineering & Computer Science Department and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Dr. Golland’s primary research interest is in developing novel machine learning and statistical inference techniques for biomedical image analysis and understanding. 

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