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Machine Learning & AI for Healthcare 2019

How blockchain and AI can work together for healthcare

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Tej Anand, professor of practice at the University of Texas' McCombs School of Business, explains how the most promising use case for blockchain in healthcare is the elimination of "redundant work, rework and reconciliation."

 

Automating ML and AI to improve speed, accuracy of predictive models

(Sponsored) DataRobot describes how advances in auto-machine learning and predictive analytics are enabling healthcare enterprises to be AI-driven.
 

AI is having real-world impact at Cleveland Clinic, Duke and beyond

Todd Schlesinger, VP of business development at Jvion, says AI has already moved beyond its status as a novel technology with exciting potential use cases, to one that is already helping solve real-world challenges and improving patient outcomes.
 

How Cleveland Clinic harnesses machine learning for its pop health goals

Joseph Dorocak, manager of population health analytics at Cleveland Clinic, describes some of the staffing and process strategies the health system uses as it puts AI to work for two intertwined aims: better patient outcomes and financial return.
 

How to operationalize AI, putting it to work for better care at lower cost

Machine learning is only a means to an end, helping organizations learn from data more quickly and intuitively, says Leonard D'Avolio, founder and CEO of Cyft.