Building AI models for healthcare (ML Tech Talks)

In this session of Machine Learning Tech Talks, Product Manager Lily Peng will discuss the three common myths in building AI models for healthcare. Chapters: 0:00 - Introduction 1:48 - Myth #1: More data is all you need for a better model 6:58 - Myth #2: An accurate model is all you need for a useful product 9:15 - Myth #3: A good product is sufficient for clinical impact 12:19 - Conversation with Kira Whitehouse, Software Engineer 34:48 - Conversation with Scott McKinney, Software Engineer Resources: Deep Learning for Detection of Diabetic Eye Disease: Gulshan et al, Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA 2016 → A major milestone for the treatment of eye disease De Fauw et al, Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine September 2018 → Assessing Cardiovas