Our recent Pistoia Alliance blog post is available
AI and machine learning (AI/ML) are certainly at peak hype at the moment around the current and future impact on Life Sciences and Healthcare. With a wealth of companies, large or small, offering new ways to interrogate existing data and build decision workflows supporting Life Science.
But is there something lacking in all the hype around AI/ML?
Maybe the biggest hindrance to using AI/ML effectively is both the volume and quality of data that exists. The models that are being built will only be as good as the data that has been used, and we know the saying ‘Rubbish in, Rubbish out’ (or variants of this ;-))
As an industry are we being overly optimistic and simplistic about the quality of data that we are feeding our smart new AI/ML pipelines? Can we assess the quality of the insights that these AI/ML tools are producing and are these tools giving any better decisions than would be obtained through other more traditional methods or human endeavours?
Photo by Scott Blake on Unsplash