Saama VP of AI Research, Malaikannan Sankarasubbu , was interviewed at SCOPE 2019 by Outsourcing-Pharma on the topic of natural language processing as a solution to pharma’s “big text problem.”
In the article, he is quoted, “Pharma has a lot of unstructured text, and the surface of this data has hardly been scratched in terms of deriving insights. There are about 27 million articles in PubMed, and 280,000 studies in ClinicalTrials.gov. Language is difficult for AI to understand.
“The reason for this complexity can be demonstrated by considering the difference between how we contextualize our thoughts into writing. This gives rise to a lot of permutation combinations that cannot be solved by traditional rules-driven programming.
“The various amounts of permutations and combinations are so huge that a rules-based system cannot handle this effectively. This is where Artificial Intelligence systems based on Deep Learning step in.”