New Algorithms Could Reduce Racial Disparities in Health Care
Machine learning programs trained with patient reports, rather than doctors’, find problems that doctors miss—especially in Black people.
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AI could make healthcare fairer—by helping us believe what patients tell us
In the last few years, research has shown that deep learning can match expert-level performance in medical imaging tasks like early cancer detection and eye disease diagnosis. But there’s also cause for caution. Other research has shown that deep learning has a tendency to perpetuate discrimination. With a healthcare system already riddled with disparities, sloppy…
Finding The Best Doctors: Garner Health Brings In $12.5M Series A For Recommendation Platform
Garner’s platform analyzes large amounts of medical insurance claims to identify the highest quality doctors in a city or neighborhood.
A closer look at the AI Incident Database of machine learning failures
The failures of artificial intelligent systems have become a recurring theme in technology news. Credit scoring algorithms that discriminate against women. Computer vision systems that misclassify dark-skinned people. Recommendation systems that promote violent content. Trending algorithms that amplify fake news. Most complex software systems fail at some point and need to be updated regularly. We have procedures and tools that help us find and fix these errors. But current AI systems, mostly dominated by machine learning algorithms, are different from traditional software. We are still exploring the implications of applying them to different applications, and protecting them against failure needs new… This story continues at The Next Web
What is semi-supervised machine learning?
Machine learning has proven to be very efficient at classifying images and other unstructured data, a task that is very difficult to handle with classic rule-based software. But before machine learning models can perform classification tasks, they need to be trained on a lot of annotated examples. Data annotation is a slow and manual process that requires humans to review training examples one by one and giving them their right labels. In fact, data annotation is such a vital part of machine learning that the growing popularity of the technology has given rise to a huge market for labeled data. From Amazon’s Mechanical Turk… This story continues at The Next Web
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