AI and Machine Learning in Cancer Medicine

AI and Machine Learning in Cancer Medicine

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The panel will focus on uses of artificial intelligence and machine learning for the early detection of cancer, its diagnosis, drug development, and clinical decision-making. These technologies have the potential to accelerate our understanding of cancer at the cellular and the systems (or "whole person") level. The AI health-care market is estimated to be $2.1 billion today and to grow to more than $30 billion by 2025. To support such growth, the biopharma industry is rapidly expanding its human capital in this area through multiple means. Advances in machine learning are improving analysis of radiological images for cancer detection, as well as analysis of pathology slides and blood samples for cancer diagnosis and prognosis. In addition, AI and machine learning are being deployed to aid in the discovery of novel therapies, optimize patient selection for treatment, and monitor for drug resistance and disease recurrence. The potential impact of artificial intelligence in oncology is great, and the panel will also explore challenges and obstacles that may delay widespread application.

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Moderator

Marc Hurlbert
Chief Science Officer, Melanoma Research Alliance

 

Speakers

Maurice Ferré
CEO and Chairman of the Board, INSIGHTEC

 

Colin Hill
Chairman, CEO and Co-Founder, GNS Healthcare

 

Mike Nohaile
Senior Vice President of Strategy, Commercialization and Innovation, Amgen

 

Susan Swetter
Professor of Dermatology and Director of the Pigmented Lesion and Melanoma Program, Stanford University Medical Center and Cancer Institute