AI Reality Check: What Can Life Sciences Firms Realistically Expect?

    The media is awash with the hundreds of possibilities that artificial intelligence could unlock across every aspect of an organisation’s operations. But what’s really possible in the near term and are companies looking in the right places for the biggest wins? IBM’s Peter Brandstetter offers some perspective

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    It’s easy to get carried away with all of the potential opportunities artificial intelligence (AI), including machine learning, natural-language processing and cognitive image processing, offer to Life Sciences. And it’s with good reason. As in the broader medical field, AI offers to cut to the chase and deliver new discoveries in a fraction of the time that human capabilities could match, for instance. 

    This is due to the scope for mining vast vaults of data spanning case notes and academic archives from across the world, and spanning decades – aided by intensely powerful processing capacity, and the ability now to store more data more affordably than ever before. And intelligent systems don’t just present a chance to scour that data for interesting correlations that would be invisible to the human eye; they can also quickly hone their efforts as they learn what to look out for.

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    As exciting as all of this sounds however, companies must have a plan for what they hope to get out of the technology. They need to ground their vision in tangible projects, and work back to a practical starting point – such as how they might optimise current systems, and rethink processes to take advantage of the best and most immediate opportunities.

    There are three areas where AI technologies are starting to have a real impact today. These include earlier detection of life-threatening conditions – for example, a prototype in which cognitive image processing helps to identify melanoma using even a simple smartphone camera. AI is also helping clinicians and Life Sciences companies understand more about disease mechanisms and disease progression. And, although it isn’t as headline-grabbing, AI technology is also starting to transform the way Life Sciences firms manage Regulatory Affairs documents and their content – for example in “understanding” written medical text in preparation for new requirements such as those set out by ISO IDMP.

    While you should let your imagination run wild, it is important to be keenly aware of what is currently possible.  You need to then determine what sort of time frame you might be looking at, and how your organization can lay the groundwork that will enable you to exploit the potential in your own activities. Additionally, look at the bigger picture of how IoT with connected devices and sensors will contribute, and further accelerate medical advancement.

    It’s a fascinating area.  If you have questions?  AMPLEXOR can help, contact us. 

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    Published on 31/07/18    Last updated on 31/07/18

    #Life Sciences, #Artificial Intelligence, #Machine Learning

    About the author

    Peter Brandstetter is a Senior Managing Consultant for Life Sciences at IBM Services in Zürich, Switzerland. An expert in applying IBM Watson AI-based analytics to big data, Peter has experience across multiple fields of IT in life sciences industry, in particular the pharmaceutical sector. His areas of specialisation are quality management and quality assurance in manufacturing and R&D (SAP QM, LIMS, ELN), enterprise content management and R&D (clinical data management, pre-clinical, R&D Lab, R&D collaboration and project management) and computer validation.

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