Episode 23 - Why AI Now?
Episode 23 - Why AI Now?
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This episode's guests: Ahmed Hosny
This episode's guests: Ahmed Hosny
Our guest is Ahmed Hosny, a machine learning research scientist working with medical imaging data from Dana Farber Cancer Institute. He is a writer and blogger and has developed a framework to evaluate AI startups in pathology.
This episode, we are talking about artificial intelligence (AI), machine learning (ML), and deep learning. We will touch on the history of AI and how it has been used to train neural networks. Pathology seems to be behind radiology with the number AI start-ups. We will find out some of the reasons why. We will discuss how the lack of format standards in imaging has slowed the growth of AI. We will examine potential uses of AI in clinical, academic and research environments.
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More About Ahmed Hosny
More About Ahmed Hosny
Ahmed Hosny, Dana Farber Cancer Institute
Ahmed Hosny is a machine learning research scientist working with medical imaging data. Currently at Dana Farber Cancer Institute where he trains and optimizes deep learning networks for the prognostication and treatment response prediction in lung cancer patients from CT images. He previously conducted research at Brigham and Women’s hospital, Wyss Institute for Biologically Inspired Engineering, as well as the MIT Media Lab. As an architect and computational designer in a former life, he spent 4 years working on large construction projects with Foster+Partners in Beijing and Playze in Shanghai. He is also intrigued by data visualization, web development, and everything UI/UX.
He is currently working on a PhD in machine learning and medical imaging at Maastricht University after having completed a Master of Design Technology at the Harvard Graduate School of Design, and a Bachelor of Architecture at the American University of Sharjah. You can find out more about Ahmed Hosny at www.ahmedhosny.com
Read Ahmed Hosny's report here: https://analogintelligence.com/artificial-intelligence-ai-startups-pathology-venture-meta-review-analysis/
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