The Many Ways Pharma Industry Is Benefitting from Artificial Intelligence

Fast forward to 2020, and we do see the pharma and biotech industry using various subsets of A.I. like machine learning and incorporating data-driven decisions. Unlike A.I. depicted in science fiction movies, particularly the famous 'I, Robots' where the AI-infused robots are out there for our throats, the reality is much brighter and safer. 

A.I. within the pharma and other industries focus on solving particular problems and assisting in conducting tasks using automated algorithms. If A.I.'s purpose in pharma could be narrowed down, then we can say that A.I. helps where humans cannot help themselves. Meaning, using A.I. for data mining and to analyze vast amounts of data for finding hidden patterns, loopholes, and information that a human eye and brain might have missed on. This helps determine new ways of treatment and drug discovery that the humans would have otherwise missed out on the opportunity. 

Furthermore, three types of A.I. have been adopted widely by the pharma and have been working as expected, driving many giant businesses forward. Those three include Data Science, Machine learning, and Deep Learning. 

Data science algorithms: This type of A.I. uses multivariate data analytics that includes the support of previously existing data and experimental values. Further, this includes using a cluster of data of, let's say, treatment outcomes and combining it with individual patient's clinical data along with medical history to come up with alternative possibilities of treatment and drug combinations that might work for that particular patient. 

Machine Learning: A subset of A.I. that relies on neural networks. A neural network includes a series of algorithms that have the ability to recognize underlying relationships in a set of data through a process that mimics the human brain operations. Neural networks also refer to neurons found within the human mind. 

Machine learning uses data-driven algorithms that allow the software to become great at predictive analysis and help the outcomes without even being explicitly programmed. 

Deep Learning: Deep learning is a subset of machine learning that also operates on neural networks but has an added layer of calculations along with combined signals. Deep learning is great for diagnostic uses as it can accurately analyze images and determine the possibilities of any present anomaly. 

Deep learning has this ability as it has been exposed to a vast amount of data and previous records for the software to be able to judge the abnormal from, let's say C.T. scans.