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.
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.Read the whitepaper
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.
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As costs continue to rise with no end in sight, what can be done to return ROI to sustainable levels?
Exploring machine learning and natural language processing.
The current wave of emerging digital technologies offers an opportunity to significantly disrupt pharma business operating models in a variety of ways.
The integration and mastery of digital technologies is becoming essential to improve the efficiency of clinical trial operations.
Using Big Data and AI to return pharma productivity to sustainable levels.
The opportunity for blockchain in healthcare and clinical trials.
Artificial intelligence (AI) technology, combined with big data, hold the potential to solve many key clinical trial challenges.
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.
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.
AI and machine learning has been put into a position to transform the pharma landscape.
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.
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.
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.
Industry experts dealing with AI services should be showing both devotion to the possibilities opened and skepticism at the same time.
To fulfill its promise precision medicine requires accurate decision support tools, especially to streamline biomarker testing so that the appropriate targeted therapies are prescribed.
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 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.
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.
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.