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Impact: AI-driven, Personalized Transparent Decision-support Tool Enables Improved Treatment Decisions
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.
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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MIT researchers have developed a methodology for systematically tracking the outcomes of academic licensing transactions as a means of increasing funding to bridging the “valley of death.”
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A new study led by MIT Sloan Prof. Andrew W. Lo finds that borrowing ideas and tools from the gaming community can improve online teaching techniques and improve learning outcomes for students.
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Cloud misconfigurations, more sophisticated ransomware, and exploitation of vendors are contributing to rising cyberattacks.
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Organizations must base their cybersecurity strategies on resilience — the ability to weather an attack with minimal damage to data, finances, and reputation.
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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
