Artificial Intelligence and how it’s changing the Life Sciences Industry
We are beginning to see Artificial intelligence, the stimulation of human intelligence processes by machines (A.I), rapidly develop within Life sciences. There are several types of machine learning and deep learning, which are subcategories of AI and their emergence in life sciences is beginning to challenge and shape investment decisions across the industry. The basic principle dictates that AI is can lead to better outcome when given a problem. AI is therefore well suited for life science applications – AI can be taught to differentiate cells, be used for higher quality imaging techniques, and analysis of genomic data.
A.I has already established a rapidly growing presence within drug discovery and development but we are now seeing it spread into the full product life cycles. The reason A.I has made such an impact is because Life Sciences is an industry full of data-rich processes and using such technology has the potential to make these processes more efficient.
The scope for A.I in Life Sciences seems to be endless from enabling drug personalisation, spotting elusive patterns to the ability to accelerate scientific breakthrough which demonstrates this is something pharmaceutical companies must start planning for.
Clinical trials are very data-intensive processes that need continuous patient monitoring to generate vast amounts of data daily; therefore, exposing these data sets to A.I can help to spot correlations and patterns more efficiently.
However, the introduction and use of artificial intelligence presents dilemmas for many of our client’s in judging the return of investment of introducing AI into their processes. Getting this right is critical in business planning terms as it often requires an assessment of the whole business model rather than a simple bolt on.
Here at Penna we believe artificial intelligence can drive positive results in terms of improving productivity and efficiency both in terms of research and in delivering more efficient manufacturing processes. The key is to understand what you are trying to achieve and the benefits you anticipate before moving into significant investment.
What is your view on Artificial Intelligence in Life Sciences?
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