Picture of Artificial Intelligence in the Pharma Industry – Looking beyond the hype

Artificial Intelligence in the Pharma Industry – Looking beyond the hype

February 2018


Artificial Intelligence (AI) can positively disrupt many of pharma's business areas and processes. From smarter drug candidate identification and repurposing older products to faster clinical trial recruitment and improved clinician/patient education and support. But pharma remains dangerously behind the AI curve and advocates say the time is now for pharma to get on board with the investment and organisational changes that will see AI deliver real productivity gains.

But before pharma can embrace this technology, it will need to make some big decisions on how it will implement AI, which vendors it should it work with, what data it needs and how will it use the results to drive quantitative decision making that is trusted. There is a lot of AI hype – but the real opportunities are identified in this compelling expert report.

Discover on this page…

  • Why this report is important to you
  • What the report will enable you to do
  • Detailed table of contents

Why this report is important to you

AI is coming of age and transforming many industrial sectors (think of the impact of driverless cars in the automotive industry). But many pharma companies have yet to fully embrace the latest AI technology/techniques or, worse, see AI as a critical capability for their organisation in the long term. But they need to. With costs rising and pressure on prices, pharma must be smarter about how it conducts its business — and AI might just be key in resolving the industry's many challenges. This report reveals the insights of AI experts who combine a deep knowledge of the pharma industry with a realistic and practical perspective on where the AI wins are for the industry now and in the future.

This report will enable you to…

  • Understand how AI can be used to streamline and improve the drug discovery process
  • Breathe new life into old products or failed late stage compounds by using AI to identify potential new indications
  • Apply AI for profiling patients to better identify clinical trial participant prospects
  • Appreciate the current AI and ML technology challenges and limitations and why trusting the "black box" is such a big issue
  • Use AI in the clinic to support HCPs and patients – could this be a boost to your "beyond the pill" support programmes?
  • Assess AI start-ups who are driving the AI service agenda to pharma, such as Atomwise, Benevolent Bio, Berg Health, Cloud Pharmaceuticals, Deep Genomics, EchoBox, Numerate, Seldon, twoXAR, WuXi and NextCODE

Expert Artificial Intelligence Contributors

The report is informed by the front-line knowledge of US/EU AI experts who work in leading innovator companies such as Cloud Pharmaceuticals, Benevolent Bio and Kadmon Group.


Table of Contents

Executive summary

Research objectives

Research Methodology

Experts interviewed

AI in pharma

  • Key insights
  • What is artificial intelligence and machine learning?
  • AI/ML technologies have been around since the 1950s so why the hype now?
  • Critical mass of data, exponential growth in computing power and cloud computing
  • Perceived benefits of AI by management
  • Accelerating the drug discovery process
  • No 'one size fits all' modality or solution
  • In house expertise versus external contractors

Application of AI by pharma

  • Key insights
  • AI applications across the whole of the pharma R&D and supply chain
  • Designing smarter drugs, quickly
  • Repurposing discarded drugs
  • Streamline clinical trials, design, recruitment and biomarker discovery
  • Enhance clinical decision making and patient engagement
  • Remote monitoring wearables and smart connected devices
  • Medication adherence and patient centricity
  • Market access

Competitive landscape

  • Key insights
  • Pharma impact
  • Pharma market activity in digital technologies and AI
  • Leading institutes in AI
  • Leading companies
  • Recent partnerships & collaborations

Key challenges for pharma to adopt AI drive approach

  • Key insights
  • Cultural change - new blood, new business strategies
  • Trusting the black box
  • Messy data - data curation and bias
  • Data sharing
  • Infrastructure and software challenges
  • How will AI affect the future of the pharma industry?
  • Accountability - social, ethical and legal issues

KOL Biographies

Figures & Tables

  • Table 1: How industries are using big data to transform their business models
  • Figure 1: Benefits of implementing AI According to Senior Executives Worldwide, June 2017 (% respondents)
  • Figure 2: AI impact across the whole of the pharma value chain
  • Figure 3: Exploit/explore HTS to optimise hit identify
  • Figure 4: Primary cause of failure for terminated compounds, 2000-2010 data pooled
  • Figure 5: Differences in the cause of failure during a) candidate nomination, , b) Phase I and c) Phase II development
  • 2000-2010 data pooled
  • Table 2: Clinical stage rediscovered with recursion platform
  • Figure 6: How will AI impact the healthcare landscape
  • Figure 7: US venture capital funding for digital health products, 2011-2016 ($b)
  • Figure 8: US venture capital funding for digital health products - most funded categories, 2016 ($m)
  • Figure 9: AI investment in healthcare and wellness, Funding 2012-2016 (in $m)
  • Figure 10: Publications in AI research 2011-2015, by country
  • Table 3: Leading institution in AI research based on publications 2011-2016
  • Table 4: AI start-up companies
  • Figure 11: Expectations for AI adoption across industries: impact on offering