The coming wave of AI-driven life sciences innovation

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Much of the current artificial intelligence (AI) buzz is around applications like Alexa and Siri, or the use of chatbots in financial services or retail. There is far less talk about what might be possible in life sciences and healthcare with the application of AI, although companies are starting to wake up to this idea. A recent study by Accenture, for example, found that 74% of life sciences executives believed that AI would result in either significant change or complete disruption of their industry within a few years. The potential savings for the healthcare industry are huge.

AI and life sciences: a match made in heaven?

AI and life sciences ought to be a natural match. There is plenty of data in life sciences and healthcare to use to train models. What’s more, some of the systems currently used to sift through data are frankly not ideal. Consider cervical screening, for example, where cytologists are expected to search manually (or visually) through hundreds, if not thousands, of slides a day to detect the one or two with possible abnormalities. The potential for false negatives in particular is very high. Appropriately-trained AI systems have potential to search far more rapidly, and with potentially more accuracy, although they also need to be checked by humans for any errors.

Just like cervical screening, diagnosis of any disease or condition is, in many ways, a form of pattern identification. There is therefore no reason why artificial intelligence should not be used to support and supplement healthcare professionals during the process. Nobody is suggesting that AI should replace doctors and other healthcare professionals, or that pattern recognition is the only answer. There is, though, no question that the use of AI could strengthen and augment human capabilities in a number of areas across both healthcare and pharmaceuticals, including diagnosis. It could even be used to help to identify the most suitable treatment for an individual patient, based on their medical history, symptoms and preferences.

Other sources suggest that AI is likely to empower patients and consumers, just as it has empowered customers in other industries. Used effectively, for example, AI systems could allow chronically-ill patients to take more responsibility for their own health, involving healthcare professionals only when necessary. The AI system could monitor particular indicators for each patient, such as blood pressure, heart rate, or blood sugar. It would provide built-in safeguards to ensure that healthcare professionals were alerted should those indicators breach individual ‘safety levels’. The point is to harness the potential of AI to increase individualisation and personalisation, and place patients at the heart of healthcare provision.

Beyond the specific

It is also important to remember that AI has the same potential in life sciences as in any other field. The same techniques can be applied across a wide range of industries, including life sciences. For example:

  • Google’s algorithm may struggle with some of the jargon around life science research, or fail to find specific new research papers, especially on sites that are not visited very often. Researchers are seeing the benefits of new machine learning-based search platforms tailored towards life science research. These enable more accurate and specific searches, improving the usefulness of the information located.
  • Pharmaceutical companies are using machine learning and AI models to predict the uptake and use of new medicines in particular markets. The idea behind this is not new, but the models, and therefore the forecasts, are better and more accurate than has been possible with other technology.

A question of how

It may well be that it is not so much a question of what AI can do for healthcare and life sciences, as how. What changes will be necessary in life science organisations and healthcare providers to enable rapid spread of AI technology? The Accenture report suggests that collaboration within ‘ecosystems’ is likely to be one of the most important developments. A massive 90% of life sciences executives believe that this will be important in future, as a way of delivering value for organisations. There are also likely to be workforce changes required to take advantage of AI.

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About Author

Olivier Zaech

Olivier Zäch is a Senior Account Advisor for the Life Science Industry. His special focus is on making technology useful for business. He helps life science companies to get tangible value out of their technology investments. He joined SAS in 1997 as a presales consultant and then held several positions including presales manager, head of client management Switzerland, business development manager, account manager Swiss SMB market, product manager internet technology, academic program manager and knowledge management officer. Before joining SAS Olivier worked at the Computing Center University of Zurich responsible for new user services such as World Wide Web, Internet Services, and statistical software and connected data bases. Olivier holds an MA degree from University of Zurich in German linguistics and literature, Computer Science and Philosophy; an MBA with a focus on international management consulting from University of Applied Sciences Northwestern Switzerland (FHNW); a Certificate in Advanced Studies (CAS) in the Management of Biotech, Medtech & Pharma Ventures from EPFL and UNIL.

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