This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
At the end of 2015, Austin Frakt, a health economist, researcher, and contributor to The New York Times’ The Upshot, wrote about the life sciences industry’s lack of interest in developing medicines that prevent cancer.
It boils down to this: There’s very little incentive for drug companies – they simply make more money prolonging a patient’s life. Clinical trials for drugs that treat late stages of cancer can yield quicker results, ultimately costing less to find out if a drug works. On the flip side, running trials for preventative therapies is a much more lengthy and costly process, often resulting in fewer positive outcomes.
To combat the deficiency in early-stage and preventative research and development, several ideas are floating around, including approving drugs based on the use of surrogate endpoints, as well as extending the period of a drug’s exclusivity to make it more financially rewarding to sponsors.