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How Might Artificial Intelligence Improve Healthcare?

How Might Artificial Intelligence Improve Healthcare?

November 20, 2017

By Richard Smith


Artificial intelligence, which few of us understand, might apocalyptically enslave humanity or release it from death. Some prominent scientists believe that robots blessed with artificial intelligence will soon be more intelligent than humans and conclude that they have little use for us. Other transhumanists think that it will be possible to “download” human minds into computers, so gifting us immortality. But in a house in a village outside Tel Aviv with a garden filled with orange trees, I encountered a more plausible and much gentler story of how artificial intelligence together with machine learning can improve healthcare and so help humanity.


Early days of development


Medial Early Sign, for which I occasionally consult, is an Israeli start-up that is using artificial intelligence and machine learning to improve healthcare. The company is funded by an individual who has made a fortune from using artificial intelligence and machine learning to predict movements in stocks and shares. The company started in 2009 with the funding and simply the concept that artificial intelligence and machine learning could be used to improve healthcare.


In the early days the company tested the concept by working with doctors in the intensive care units in two Tel Aviv based hospitals. The company used data, artificial intelligence, and machine learning to predict which patients would die and which would develop kidney failure much more accurately than the doctors. But these experiments were simply for proof of concept; they were not of business interest. It allowed the company to better understand the complexity of medical data and adjust its algorithmic tools and methodologies.


The four stages of developing algomarkers, predictors of health events


At the heart of the enterprise is developing algomarkers that will predict the risk of future health events from routinely collected data—for example, the development of colon cancer or the onset of complications in patients with diabetes.


The four stage process of developing algomarkers begins with establishing a pipeline of possible algomarkers that would be clinically useful, actionable, capable of being produced from routinely available data, and lead to a return on investment, meaning that health authorities would be willing to pay for the algomarker and that it would eventually lead to profits for the company. This pipeline is developed mainly from continuing conversations with clinicians. Possibilities that seem clinically exciting might have to be discarded at this stage because they don’t meet the criteria.


Read the full post here.