Artificial Intelligence in the Pharmaceutical Industry
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.
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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
AI in pharma
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
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
Pharma market activity in digital technologies and AI
Leading institutes in AI
Recent partnerships & collaborations
Key challenges for pharma to adopt AI drive approach
Cultural change - new blood, new business strategies
Trusting the black box
Messy data - data curation and bias
Infrastructure and software challenges
How will AI affect the future of the pharma industry?
Accountability - social, ethical and legal issues
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 offerings
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