AI startups for Data Aggregation and Analysis during Drug Development
Even though public opinion and reputation of pharmaceutical companies appear to be eroding and a significant decline in the public’s perception of the transparency, openness and authenticity of drug makers “is in the air, everywhere I look around”, pharmaceutical companies do play a positive role in society. So, despite corruption and lack of transparency, the biggest problem of pharma is its conservative nature while dealing with a process — drug development — that simply doesn’t work anymore due to the lack of innovation, amid digital disruption, rapid technological advances and other issues such as lack of data reproducibility. Accordingly, “an army” of AI pharma startups is being set up to deal with pharma’s problems.
It’s quite possible to say that the pharma industry and academia have historically done a poor job of managing their data. But data, both preclinical and clinical, are both a huge asset and a big “messy” problem (Replication or Reproducibility Crisis, Hidden Results, Negative Results, Raw Results, Ghostwriting, In Silos) for the pharma and academia. So, improving data lifecycle is undoubtable a priority.
