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SHOWCASE: AI in Life Sciences – the talent challenge

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Neha Rajdev : Business Intelligence Associate Neha Rajdev
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The AI disruption of healthcare

artificial intelligence
noun
noun: artificial intelligence; noun: AI
the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

“AI Can Detect Alzheimer’s 10 Years Before Symptoms Show Up”… “Robots and AI Will Take Over These 3 Medical Niches”… “Two AI Medtech Startups Bag New Funds”… “System Uses Artificial Intelligence To Help Detect Melanoma Earlier”

Artificial Intelligence is now grabbing the headlines, not only in Life Sciences but across all industries, as the application of disruptive technologies gather momentum. Healthcare (along with retail and financial services) is set to reap some of the biggest rewards from adopting artificial intelligence-based solutions1, with growth in the AI health market specifically, expected to reach $6.6 billion by 2021 (with a CAGR of 40%)2.

From using machine learning algorithms and predictive analytics to reduce drug discovery times, to providing virtual nurses and advancing diagnostics, AI is enhancing functions and processes across the entire healthcare landscape at a rapid pace. Machine learning and data processing quickly allow the conversion of vast volumes of biomedical and healthcare data into solutions that could potentially transform clinical trials and our understanding of diseases such as cancer and diabetes. Faster, more accurate diagnoses would further enhance personalised medicine at every stage of the patient journey.  From a consumer perspective, AI technology can more quickly decipher behavioral trends, allowing Life Science players to closer engage with the patient and provide better treatment monitoring. In the longer term, many feel that AI-enabled technology could potentially see the clinician replaced in varying capacities as robots carry out laboratory research, diagnosis and even selective treatment.

These advances have meant that the healthcare landscape is transforming exponentially. Large life science players, GlaxoSmithKline (GSK), AstraZeneca (AZ) and Amgen for example, have all recently turned to AI through acquisition or collaboration, as its adoption presents operational and commercial opportunities across their own businesses. In August of this year, GSK announced a partnership with Insilico Medicine to explore how the latter’s artificial intelligence technology could aid in the drug discovery process. The collaboration marked GSK’s second AI deal within the space of a month; in July 2017 they inked a drug discovery collaboration with UK-based AI and big data processor Exscientia. In the same way, in August 2017, AZ entered a research collaboration with US-based BERG, in which AZ could approach drug discovery in an innovative new way using BERG’s artificial intelligence platform to advance their CNS therapeutic development. Life Science AI has also witnessed a surge of interest from the investor community; a recent CBInsight study reports that deals with AI in healthcare startups increased 29% year-over-year to hit 88 deals in 20163; the report goes on to list 106 start-up companies transforming healthcare through AI. Some companies have even created new venture funds, such as Google’s Gradient Ventures, to foster AI startups from all industries including healthcare. Entrants from outside the Life Sciences are also getting in on the action through acquisition, as observed recently by technology pioneers such as Google, Qualcomm, IBM and Amazon.

Addressing the talent challenge

As AI edges towards to the frontline of healthcare, Coulter Partners continues to build its experience and involvement with some of the leading AI Life Science companies, to help fulfil their senior hiring requirements. Recent clients, all with an AI component within their business, include:

  • a UK-based developer of testing software used for clinical trials and diagnostic screening which has recently combined AI technology with cognitive neuroscience (Sales Leader)
  • a provider of cloud-based genomics analysis platforms (VP Sales, Chairman & Board Director)
  • European life-sciences company leading population-based genome research studies using advanced robotic systems and next-generation sequencing (five VP-level roles)
  • a platform operator building human machine interfaces that combine VR, computer graphics, brain imaging and machine learning (Chief Operating Officer)
  • a US technology company providing applications and machine learning-based health informatics for remote health and patient management (Non-Executive Director)
  • an Asia-Pac healthcare organisation providing intelligent clinical data to support physicians working with remote populations (Chief Executive Officer)

So, what are the implications for healthcare talent within AI as the convergence of technology and science continues to grow? What will this disruption mean to healthcare AI organisations as they compete to hire and retain high-calibre senior leadership? A recent McKinsey survey of 3,000 AI-aware companies found “even if you’re not an IT leader or an analytics leader, impact in these organisations has to be led from the top”4. AI adoption across the Life Sciences clearly requires an understanding of the data-science alongside the clinical, regulatory and patient processes. Top-level leaders will additionally require a holistic approach and need to demonstrate cross-functional expertise to integrate the data analytics and life science within the commercial context of the business. Communication and collaboration will be viewed as key attributes for successful senior leaders as a multitude of required competencies consolidate.

To better understand the typical career path of today’s Life Science AI leader, Coulter Partners recently conducted an analysis of the top leadership in some of healthcare’s leading AI companies5. The results illustrated that Life Science career backgrounds are rare, with only a quarter of our dataset possessing any experience within either the pharma, biotech or medtech industries. By contrast, nearly a third had software or engineering industry experience and a considerable cohort had a background in Financial Services, Academia and the Not-for-Profit sector. Requirements for success in this field are clearly multi-faceted; the evidence indicates an affinity with the technology. Can growth in this segment be sustained however if companies do not have the correct blend of leadership skills and Life Science acumen at the helm ?

While there is a great deal of hype around Artificial Intelligence and its potential to disrupt, it is becoming clear that companies must develop the correct blend of capabilities in-house, attract the best-qualified external talent and continue to develop technology simultaneously, to facilitate a successful AI-enabled business and trigger change in the organisation. How will organisations evolve with perfect symbiosis of scientific talent and transformational technology while continuing to integrate with existing teams, cultures and traditional business models? Will the clinician ever be completely replaced by the robot?

Sources:

  1. Sizing the prize: What’s the real value of AI for your business and how can you capitalise?, PwC, June 2017
  2. Artificial Intelligence: Healthcare’s New Nervous System, Accenture, June 2017
  3. Up And Up: Healthcare AI Startups See Record Deals, CBInsights, August 2017
  4. Artificial intelligence in business: Separating the real from the hype, McKinsey, November 2017
  5. Coulter Partners proprietary analysis of CEOs of 27 global AI Healthcare leading companies, October 2017. Experience can be in more than one segment.

 

 
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