Vaccines are an especially risky proposition for pharmaceutical manufacturers since the costs are higher than for other types of drugs and demand could drop drastically once a crisis is over.
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MIT study explores the key factors behind patient outcomes in clinical trials evaluating new treatments for non-small-cell lung cancer.
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The studies describe new methods for accelerating drug approvals during pandemics and for providing more accurate measures of the probabilities of success for clinical trials of vaccines.
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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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The MIT Laboratory for Financial Engineering (LFE) and Informa Pharma Intelligence announced a new initiative, Project ALPHA (Analytics for Life-sciences Professionals and Healthcare Advocates).
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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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.
