event_note Friday, 11 Oct 2019
query_builder 15:45 - 16:45
card_travel 'How To'
mic English
Artificial intelligence (A.I.) has already established a small but growing foothold in the life sciences industry, starting with drug discovery and development and now in emerging applications across the product life cycle. Opportunities extend across several data-rich processes generating the expectation that A.I. will bring a constant stream of disruptive changes to our sector.

A.I. represents an opportunity for life sciences companies to transform business at all levels — organisation structures, processes, and people. However, companies must have a plan for what they hope to get out of the technology. They need to establish tangible projects and identify a practical starting point to take advantage of the best and most immediate opportunities, to explore a future not yet imagined.

While most in the life sciences sector agree that A.I. is likely to reshape our industry in the longer-term, the technology is still relatively in its infancy despite some benefits starting to emerge. Pockets of early adopters are driving new approaches and seeking a competitive edge to accelerate innovation and patient outcomes, but as yet these are in the minority of firms. A.I. will not be a great disrupter due to its prevalence, but rather, how effectively firms employ A.I. will determine the biggest winners.

Our How-to session will bring together thought leaders in the sector to explore A.I. related trends, when to embrace them and how to prepare for the future, as well as:
  • Lack of talent – both A.I. developers and multi-disciplinary staff are in demand and short-supply. How can the sector attract and nurture the necessary skills?
  • Are Start-ups and tech companies more likely to take the lead in A.I. powered drug discovery the rate of progress in modern machine learning outpaces the rate of progress in the pharmaceutical sciences
  • Can we expect Big Pharma to accelerate acquisitions within the sector?
  • How can we holistically view the potential interactions between A.I., machine learning, natural language processing, deep learning, big data and predictive analytics?
  • Having an inherent knowledge of how A.I. to understand how to define practical objectives for the utilisation of the technology
  • Adopting disruptive technology in a highly regulated industry
  • Are sensors / data capture, Big Data handling and assisted interpretation the future of clinical / patient interactions?
Avgoustinos Filippoupolitis
Senior Machine Learning Data Scientist
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Tobias Kloepper
CEO & Co-Founder
Aigenpulse Limited
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Ewa Truchanowicz
Managing Director
Dignio Ltd
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