In this episode of DataFramed, a DataCamp podcast, Hugo speaks with Arnaub Chatterjee. Arnaub is a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company. They’ll discuss cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare by looking at practical applications and how McKinsey & Company is helping the industry evolve.Tune in for an insider’s account into what has worked in healthcare, from ML models being used to predict nearly everything in clinical settings, to imaging analytics for disease diagnosis, to wound therapeutics. Will robots and AI replace disciplines such as radiology, ophthalmology, and dermatology? How have the moving parts of data science work evolved in healthcare? What does the future of data science, ML and AI in healthcare hold? Stick around to find out.LINKS FROM THE SHOWFROM THE INTERVIEWMcKinsey Analytics on TwitterHot off the press article for HBR’s Future of Healthcare online forum (By Arnaub Chatterjee)Our latest piece on the promise & challenge of AI (By James Manyika and Jacques Bughin)Are robots coming for our jobs? (mckinsey.com)Analytics Careers page (mckinsey.com)How we help clients in healthcare analytics (mckinsey.com)AI analysis of 400+ use cases, including ones in healthcare (By Michael Chui et al. mckinsey.com)FROM THE SEGMENTSMachines that Multi-task (with Manny Moss)Part 1 at ~21:05Responsible AI in Consumer EnterpriseHilary Mason, DJ Patil and Mike Loukides on Data EthicsEthicalOS TookitPart 2 at ~40:0021 Definitions of Fairness Tutorial from FAT* (Arvind Naranayan)Kate Crawford's keynote address "The Trouble with Bias" from NIPS 2017The (im)possibility of Fairness (Sorelle et al. arXiv.org)Learning from disparate data sources (Li Y et al. PubMed.gov)Distributed Multi-task Learning (Liyang Xie et al. KDD.org)The Cost of Fairness in Binary Classification (Aditya Krishna Menon et al. proceedings.mlr.press)
Original music and sounds by The Sticks.