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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."

 

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.

 

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.
 

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.
 

Digital health 'sherpas' helping guide the journey of bringing AI to healthcare

Dr. Sujay Kakarmath, digital health scientist at Partners HealthCare Pivot Labs, explains how the Boston organization is enabling collaboration between hospitals, health systems and other stakeholders such as big tech and pharma.