Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
Three factors to unlock the value of AI for life sciences
Posted on May 1st, 2019 by Christy J. Wilson in Pharma R&D
Artificial Intelligence (AI) has been at the top of many boardroom agendas for the last several years, as execs try to integrate the technology. Recent RELX research found that, across all industries, 88 percent of senior executives believe AI will help their businesses be more competitive. In the life sciences, AI has the potential to scale the benefits of precision medicine or automated disease prediction to more patients across the world, and could generate more than $150 billion in savings for the healthcare industry by 2025.
However, the news wasn’t all good. Only 56 percent of organizations are currently using AI or Machine Learning (ML) and of those, under half (39 percent) believe they are getting the most value out of it. This shows there is still a way to go before the technology’s potential is realized, so businesses looking to get value from AI and ML must consider the following:
- Improved collaboration: Deploying AI successfully requires far greater collaboration across different disciplines and geographies. As Michael Kratsios, deputy CTO of the White House’s Office of Science and Technology Policy stated,“to realize the full potential of AI for the American people, it will require the combined efforts of industry, academia, and government.” Through collaboration, industries will benefit from shared insight and be able to further AI development far quicker than going it alone.
- Commit to spending: The research found only 18 percent have concrete plans to increase their AI investments. But investment is crucial in overcoming barriers such as the growing skills shortage – by next year it’s estimated there will be 2.7m unfilled data science jobs. This is particularly worrying for pharma as tech-savvy workers are already opting for better remunerated jobs in other sectors like banking, leaving them even more exposed.
- A platform for success: We’ve seen several generalist AI platforms suffer notable set-backs when it comes to life sciences, because they are not designed to handle scientific data. Because of the difficulties involved in processing different types of data, one tool which provides a singular experience cannot meet the needs of multiple different researchers. Businesses must look to specialized AI platforms to make the most out of the technology.
AI offers industries a chance to find solutions to some of their most difficult challenges, but without careful consideration of the next steps, AI will fail to reach its full potential. Get in touch to find out how Elsevier can help you overcome the barriers to successful AI implementation.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
Christy J. Wilson
Sr. Director, Pharma and Biotech Segment
Connect on LinkedIn
Follow on Twitter
- Webinar: Using machine learning to identify adverse events from scientific literature
- Elsevier supports Amsterdam’s AI initiatives
- Life Sciences trends that make me excited for 2020 (and beyond)
- Amsterdam goes all-in on AI
- Finding the Corpus of Knowledge for Machine Learning/AI In Chemistry