The first month of 2019 is not over yet, but there are already four major announcements about new research projects between large drug discovery corporations and smaller artificial intelligence (AI) companies -- this is more than the number of all similar announcements in the year 2014 combined -- only three.
Over the last five years the interest of pharmaceutical professionals towards machine learning (ML) and artificial intelligence (AI) has measurably increased -- while only one “AI-related” research collaboration involving “big pharma” appeared in the news in 2013, the number of such events increased up to 21 in 2017 alone, involving some of the top pharma players like GSK, Sanofi, Abbvie, Genentech, etc.
Machine learning and artificial intelligence have become widely discussed topics in the area of life sciences and healthcare over the last several years. While a lot of pharmaceutical companies and healthcare organizations express considerable interest in possible new opportunities, associated with the use of artificial intelligence for early drug discovery, clinical trials optimization, and business intelligence, a considerable gap still exists when it comes to understanding new technologies by pharmaceutical professionals and leaders. The key questions here are these:
What machine learning / AI can and can’t do for pharmaceutical industry
What should be done to harness practical and measurable value out of machine learning / AI?
How it should be done and what are the timelines for getting returns on investments?
(Last updated: 23.08.2018)
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China has been becoming a research powerhouse in many fields of science, but it still is a minor player when it comes to pharmaceuticals. However, recent developments suggest that China’s role in pharmaceutical research may change in the near future.