AI Insights
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.
Applying AI to manage the risks and costs of postmarketing requirements
Our award-winning Cassandra AI system harnesses real world data on drugs and data obtained from the US FDA and EMA postmarketing requirements databases to accurately forecast whether postmarketing studies will be necessary.
Advancements in AI for site selection
Learn how ICON is using human-enabled AI to efficiently integrate, interrogate and interpret large datasets from diverse sources and gain valuable insights for site selection in both expected and unexpected ways.
Artificial Intelligence at ICON
Artificial intelligence (AI) is a general term for software that mimics human cognition or perception. Rapid progress in AI is transforming many industries with considerable impact, including clinical trials. In this webinar learn how ICON is using AI to improve industry processes and help solve problems people didn’t once think possible.
Advancements in AI for optimal clinical trial site identification: Turning Big Data into actionable intel
Despite advancing technologies, clinical trials still struggle to meet patient enrolment goals. Discover how advancements in AI can help pharma and biotech identify the best sites for their clinical trials through the harnessing of Big Data.
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.
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.
AI and clinical trials blogs and media contributions
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Blog: How human-enabled AI is creating a new map for navigating site selection
Discover how AI transforms site selection in clinical research by leveraging data and human expertise through an ecosystem approach to optimise site ranking, patient enrolment, and study timelines.
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Blog: How AI could transform literature surveillance for pharmacovigilance
In pharmacovigilance AI could be used to detect adverse events sooner, offering benefits for patients and researchers.
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Media article: AI has the power to revolutionise drug discovery and design
In this article from the Irish Times, Gerard Quinn, VP of IT Innovation and Informatics provides insight on how artificial intelligence can support the drug development process in order to deliver new treatments to more people, faster.
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Media article: Integration of AI in imaging clinical trials
In this article from Drug Discovery World, Sophie Winandy, Executive Director of Medical Imaging, explores the benefits of AI and how it can be integrated throughout medical imaging within clinical trials.
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AI In Clinical Research: Now And Beyond
In this piece from Forbes, Dr. Greg Licholai, Chief Medical and Chief Innovation Officer talks about recent developments, regulatory considerations, and the promising future of AI in clinical research are reshaping the landscape of drug development and patient care.
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Media article: AI is game changer for toughest areas of drug discovery
In this piece from Forbes, Dr. Greg Licholai, Chief Medical and Chief Innovation Officer talks about how, with proper use, AI can help accelerate a new era of medical research.
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Media article: AI poised to revolutionise drug development
ICON’s Chief Medical and Innovation Officer, Greg Licholai, explores how AI is poised to revolutionise drug development process.
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Blog: How data science is changing the face of healthcare
In this podcast, Michael Goedde shares his journey in technology and his role in building the digital platform for healthcare systems at ICON.
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Media article: The role of digital health technology tools in supporting medical adherence
In this article in Pharmaceutical Market Europe, Dr Peter Schueler and Dr Isaac R Rodriguez Chavez outline the role of digital health technology tools in supporting medical adherence.
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Blog: Voice enabling participant diary collection powered by AI on AWS – Part 2
This blog outlines considerations for integrating voice-enabled diary data within a clinical trial, and serves as an example of how ICON applies innovation to enable efficient and effective patient participation in clinical research.
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Media article: The silent problem - Machine learning model failure
This article outlines how to diagnose and fix ailing machine learning models.
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Media article: The decentralisation of clinical trials is a key catalyst for innovation in artificial intelligence
Michael Philips considers the impact of decentralised clinical trials on innovation in AI.
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Media article: Will big tech dominate healthcare in the years ahead?
Louisa Roberts, VP of Corporate Development and Partnerships, considers the benefits of partnerships with tech companies.
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Media article: The path to smarter digital health
Michael Phillips and Kevin Rooney discuss the use of artificial intelligence for the purposes of automation in digital health and pharma.
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Blog: Can AI improve R&D productivity enough to restore ROI to sustainable levels? Only if we carefully manage its deployment
As costs continue to rise with no end in sight, what can be done to return ROI to sustainable levels?
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Blog: Five applications of artificial intelligence to enhance RWE generation
Exploring machine learning and natural language processing.
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Blog: Top five digital technologies set to transform pharma R&D
The current wave of emerging digital technologies offers an opportunity to significantly disrupt pharma business operating models in a variety of ways.
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Blog: Harnessing big data: the raw material of digital transformation
The integration and mastery of digital technologies is becoming essential to improve the efficiency of clinical trial operations.
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Blog: Pharma ROI restoration
Using Big Data and AI to return pharma productivity to sustainable levels.
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Blog: Harnessing blockchain technology and digital disruption in clinical trials
The opportunity for blockchain in healthcare and clinical trials.
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Blog: The power of AI to transform clinical trials
Artificial intelligence (AI) technology, combined with big data, hold the potential to solve many key clinical trial challenges.
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Blog: Alexa… What’s the deal with voice assistants?
The idea of talking with a computer has been in the popular imagination for decades, and only in the last few years are we seeing science fiction become reality.
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Blog: Leveraging voice-assistant technology in clinical trials
Our industry seeks to make trial participation simpler, more convenient and more patient-centric. One of the enablers of this simplification is the novel use of new technology.
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Media article: BioITWorld: Keeping it real - Challenges and benefits of integrating AI and Machine Learning into Pharma R&D
AI and machine learning has been put into a position to transform the pharma landscape.
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Media article: 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.
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Media article: Role of data in revolutionising the CRO industry
Tom O’Leary, chief information officer (CIO) of ICON, highlights how data is already revolutionising the CRO industry and the steps that still need to be taken to better match patients with targeted therapies and improve health outcomes.
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Media article: AI in Life Sciences – Covid pushes AI driven clinical trials and drug manufacturing into the limelight
In analyzing clinical trials and drug manufacturing, it is apparent that adoption of AI and machine learning technology holds astonishing potential to improve the healthcare sector.
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Media article: The danger of 'going too far' with AI
Industry experts dealing with AI services should be showing both devotion to the possibilities opened and skepticism at the same time.
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Media article: Using clinical decision support tools to facilitate decision-making in precision medicine
To fulfill its promise precision medicine requires accurate decision support tools, especially to streamline biomarker testing so that the appropriate targeted therapies are prescribed.
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Media article: Data science: transforming the future of clinical trials
Machine learning and artificial intelligence have the potential to add much value to a clinical trial by facilitating informed decision-making, reducing the time to complete the trial, and the overall drug development process.
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.
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
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.