Using data science to forecast clinical trial outcomes
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MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process
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MIT Sloan and CSAIL researchers apply artificial intelligence techniques to one of the largest datasets of clinical trial outcomes to handicap the drug and device approval process
Researchers launched an in-house Data Science and Artificial Intelligence (DSAI) challenge to beat MIT’s machine-learning models for predicting clinical trial outcomes. The results are now available.
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AI is a tool to get things done. To use it properly and generate value, organizations need the right capabilities — including a good understanding of data.
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In a new book, MIT roboticist Daniela Rus looks at the powers and limitations of robots and how humans can work with them to unlock new capabilities.
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Car owners, on average, would want $3,300 to give up ownership and use of their vehicle for a month during the pandemic.
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Machine learning is a powerful form of artificial intelligence that is affecting every industry. Here’s what you need to know about its potential and limitations and how it’s being used.
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Companies make common data science mistakes. Here’s an expert’s guide to what they are and how to avoid them.
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In a new book about the history of Kendall Square from the MIT Press, author Robert Buderi chronicles the area's biggest successes in innovation.
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Matt Beane, SM ’14, PhD ’17, argues those using artificial intelligence will become incrementally de-skilled unless they are consciously upskilling at the same time.