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AI and clinical trials

Artificial Intelligence (AI) technology, combined with big data, hold the potential to solve many key clinical trial challenges.

Big Data and AI technologies are complimentary as AI can help to synthesize and analyse ever-expanding data.

AI-powered capabilities, including data integration and interpretation, pattern recognition and evolutionary modelling, are essential to gather, normalise, analyse and harness the growing masses of data that fuel modern therapy development. Indeed, AI and advanced analytics were viewed as the digital technology with the most potential to improve clinical R&D productivity in our Digital Disruption in Biopharma industry survey.

AI has many potential applications in clinical trials both near- and long-term. AI technologies make possible innovations that are fundamental for transforming clinical trials, such as seamlessly combining phase I and II of clinical trials, developing novel patient-centered endpoints, and collecting and analysing Real World Data.

The impact of artificial intelligence on outcomes based contracting

The impact of artificial intelligence on outcomes based contracting

In the United States outcomes-based contracting (OBC) has long been proposed as a measure to reward innovation, based on actual performance of treatments and interventions in patient populations. However, the perceived and actual challenges in implementation have prevented many innovative contracts from leaving the drafting table.

Recently, the potential use of artificial intelligence (AI) to predict suitable outcomes for patients to mitigate potential challenges has been discussed. Read our whitepaper for insights on the latest trends and challenges.

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The power of AI to transform clinical trials

The power of AI to transform clinical trials

The AI transformation of clinical trials starts with protocol development, reducing or replacing outcome assessments that may be more responsive to change than traditional methods and utilising remote connected technologies that reduce the need for patients to travel long distances for sites visits.

Data-driven protocols and strategies powered by advanced AI algorithms processing data collected from mobile sensors and apps, electronic medical and administrative records, and other sources have the potential to reduce trial costs. They achieve this by improving data quality, increasing patient compliance & retention, and identifying treatment efficacy more efficiently and reliably than ever before.

How AI and other digital technologies will transform R&D productivity enough to restore ROI

How AI and other digital technologies will transform R&D productivity enough to restore ROI

In our recent white paper 'Digital Disruption in Biopharma' almost 80% of survey respondents were using, or planning to use, AI technologies.

Two thirds of industry executives surveyed were bullish on the potential of AI to increase productivity by 26 percent or more. 22% of respondents were expecting a 51% to 99% improvement, whilst 5.5 percent were expecting an improvement of 100% or more.

Related blog: Can AI improve R&D productivity enough to restore ROI to sustainable levels? Only if we carefully manage its deployment

Related article: BioITWorld: Keeping it real - Challenges and benefits of integrating AI and Machine Learning into Pharma R&D


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Can digital technologies and AI improve R&D productivity enough to restore ROI?

A thought leadership article authored by Tom O’Leary and featured in BioSpectrum Asia October 2019 edition, exploring what can be done to help ensure return on investment in clinical trials. The mean cost of bringing a new pharma product to market has risen from $1.1 billion in 2010 to $2.1 billion in 2018– with clinical trials making up a large and growing share. 

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Five applications of artificial intelligence to enhance RWE generation

Exploring machine learning and natural language processing

As the availability of big data continues to grow exponentially, new technologies to process these data sets have become available. These include Artificial Intelligence (AI), Machine Learning and Natural Language Processing (NLP).

What are their differences, what operational processes do they perform and what are the advantages of implementing these innovative technologies in a centralized and secure RWE technology platform? Read our blog to learn more.

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Panel: Digital R&D: AI - the reckoning?

Andrew Garrett, Executive Vice President Scientific Operations, ICON, joins Badhri Srinivasan, Head, Global Development Operations, Novartis and other panelists debating where will AI add value to pharma, and the complexities of implementation, the issues of data collection, quality and the need for scale. Moderator: Sarah Neville, Global Pharmaceuticals Editor, Financial Times. Recorded late November 2019 at the Financial Times Global Pharmaceutical and Biotechnology conference in London. 

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 Leveraging voice-assistant technology in clinical trials

Leveraging voice-assistant technology in clinical trials

In addition to the rise in mobile and wearable solutions, AI powered digital voice assistants are becoming ubiquitous, with every smartphone today now shipping with either Siri or Google Assistant, while smart speakers like the Amazon Echo with Alexa and Google Home are becoming the hubs for smart homes.

Voice assistant technologies provide an opportunity to create a different level of engagement and interaction with patients in comparison to regular apps and web pages. ICON have developed a proof-of-concept application operating on the Amazon Echo platform that leverages a Voice Assistant to deliver a patient-reported outcome instrument and collect patient responses.