5 Areas Driving AI Adoption in Life Sciences
Learn how life science organizations are adopting and applying artificial intelligence across their people and processes.
In This Article
From discovery to distribution, artificial intelligence (AI) is radically transforming life sciences by impacting processes and people throughout the value chain with wide-reaching implications.
AI can enable researchers to discover insights hidden within large and complex data sets, which in turn can increase clinical success rates. In the manufacturing process, for example, AI can identify quality control issues and solve for bottlenecks before they affect production and operations. The supply chain can also benefit from AI by automating demand and supply planning as well as enabling better decisions around product distribution.
Research organizations and life science companies are increasingly leveraging AI to drive better operational, clinical and business outcomes. Ninety percent of large pharmaceutical companies initiated AI and machine learning (ML) projects in 2020 compared to 60 percent of emerging biopharma companies.
AI is accelerating processes, insights and operations across the life sciences value chain — from optimizing pre-clinical and clinical research, to improving manufacturing, distribution and commercialization.
Let’s take a look at a few of the applications driving AI adoption in life sciences.
Few technologies have completely altered how pre-clinical and clinical discovery work is conducted as much as AI. With only 14 percent of drugs in clinical trials making it to FDA approval, life science companies continue to double down on improving drug discovery cost and success rate.
In the dry lab, AI/ML allows computational biologists to create models that exponentially increase the accuracy of, and speed to, target cell identification.
In the early R&D lab, AI/ML helps make predictions based on large data sets to rapidly identify novel molecule and drug relationships to drive focused, faster discovery.
In clinical trials, AI is being used to optimize trial design, improve patient selection and retention, monitor drug adherence, and proactively identify risk and opportunities throughout the trials.
AI is also impacting how researchers access and interpret the enormous volume of image data generated during research to accelerate new insights. AI techniques are improving the process of acquiring images as well as image quality, such as leveraging data to advance high resolution microscopy.
AI can process huge volumes of imagery at a fraction of the time and do so with a higher level of accuracy, thereby eliminating human error.
Natural language processing
A significant portion of life sciences data is unstructured — a number that continues to increase each year. Natural language processing (NLP) can not only save time by automating literature and report review, but it can also rapidly extract key information from unstructured text to enable faster analysis and synthesis of data for actionable insights.
By speeding up literature research and review, NLP can create a significant time to market advantage and eliminate human error from the process.
You may have seen DeepMind’s announcement in recent months about AlphaFold’s ability to accurately predict how a protein will fold. If the findings hold up to peer review, this is an example of how AI can enhance the study of structural biology and how it can accelerate the understanding of cellular building blocks for faster and more targeted drug discovery.
By providing insight into the genes that contribute to medical conditions, genomics is leveraging AI to enable more individualized treatment. AI gives scientists the ability to better understand complex diseases at the genetic level. Successfully interpreting and acting on that genomic data that will inform care decisions.
AI is impacting all areas of life sciences — from leveraging massive biological data to speed discovery, to ensuring the quality of manufacturing processes and efficiently bringing therapies to market. Regardless of the application, life science organizations need to ensure that their infrastructure can support the faster development cycles, improved model accuracy and higher GPI utilization that AI demands.
WWT helps life sciences organizations leverage data to inform and accelerate impactful treatment discovery, manufacturing, distribution, marketing and sales — from strategy to solution deployment. Connect with our Life Sciences experts to learn how.