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    Home > Biochemistry News > Biotechnology News > Can artificial intelligence replace human clinical trials?

    Can artificial intelligence replace human clinical trials?

    • Last Update: 2021-11-14
    • Source: Internet
    • Author: User
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    Emerging technologies such as artificial intelligence have received widespread attention in the field of new drug research and development, and concepts such as "virtual clinical trials" are also emerging in endlessly


    The 2013 Nobel Laureate in Chemistry, Yalier Vaschel, focused on the key role of computer technology in the new crown epidemic and the pharmaceutical industry: "The huge computing power we have today can solve many problems that could not be solved in the past


    Taking the new coronavirus as an example, Vachel and his research team used computational research methods to predict the possibility of virus mutations in June last year, and carried out related research before the mutation actually appeared


    Yang Zhantao, deputy secretary-general of the World's Top Scientists Association and senior scientist of the World's Top Scientists International Joint Laboratory, mentioned the application of artificial intelligence to the research and development of new drugs


    "Artificial intelligence is very hot now, especially in a field that has been a big fire in the past three years


    "Artificial intelligence is just an advanced technology that can shorten the time of drug development, or it can help clinical trials collect clinical data to build models, and then predict whether the drug will succeed.


    "Clinical trials should be conducted in humans, not artificial intelligence


    One drug will not be suitable for all patients, so how can patients benefit? Ten thousand of the millions of compounds may be the final drug, but its receptor may only be suitable for a small population


    "In the process of clinical trials, due to the relatively small sample size, we need to use artificial intelligence to calculate what the drug will look like in a large number of other patients


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