Discover the positive impact of Artificial Intelligence in Clinical Trials driving success.

Clinical trials are the cornerstone of medical research and drug development, but they face significant challenges that can impede progress and drive-up costs. Trials are notoriously expensive, often running into the hundreds of millions of dollars, and the success rate is dismally low, with only a small fraction resulting in approved drugs. Compounding these issues, more trials than ever are competing for a limited pool of eligible patients, leading to recruitment bottlenecks and under-enrollment. Additionally, the processes involved in conducting clinical trials are still very manual and siloed, especially when compared to other industries that have embraced digital transformation.   Despite these hurdles, the recent successes of COVID-19 vaccine development have shown that it is possible to run rapid and effective clinical trials under the right conditions. 

Recent advances in AI have created new opportunities to address many of these challenges through technology.  New AI driven approaches can reduce the risk of failure, lower cost, and increase execution speed.  However, the integration of AI must be done thoughtfully to ensure that patient safety, ethical considerations, regulatory compliance, and the integrity of efficacy detection are not compromised.  At Veridix AI, we are bringing such capabilities into today’s clinical research to enable faster scientific advancements through our technology and AI platforms combined with our 47-year-old tradition of scientific excellence. 

Modernizing Clinical Research

De-risking Trials Through AI

AI can mitigate common causes for failure of a clinical trial.  For example, under-enrolling sites can put the statistical validity of the trial’s results at risk.  AI can help mitigate this risk in a few ways.  Real-world data (RWD) such as clinic notes and electronic health records (EHRs) can be analyzed by large language models (LLMs) to identify eligible participants and associated sites or geo-locations.  AI can also be used to predict site and participant burden, informing adjustments to the protocol design which increase willingness to participate in the trial and improve the likelihood that site staff will prioritize the trial over others.  To reduce dropouts, AI-driven insights can help identify at-risk participants and can improve retention rates by providing personalized reminders, educational content, and support through chatbots and mobile apps. These tools help participants stay informed and motivated, ensuring compliance with the protocol.  Adopting such a multi-pronged strategy can enhance the likelihood of clinical trial success.   

Changing the Cost Structure

AI can also help reduce the ever-increasing cost of clinical trials.  The promise of increased efficiency through technology has paradoxically paralleled rising operational costs in today’s clinical trials, with technology expenses carving an ever-growing portion of trial budgets.  Studies typically require 8-12 different systems to capture data and facilitate clinical operations.  The adoption of point solutions combined with rigid, manual data management processes has led to costly integrations and time-consuming reconciliations which increase time to market for new therapies.  It’s within this context new AI capabilities have arrived on scene, a clinical trial ecosystem ripe for intervention.  GPT-4 has shown the capability to aid in data exchange by automating data transformations from unstructured or site-specific formats into emerging standard formats such as HL7 FHIR.  This approach can lead to increased use of RWD in clinical trials, for example, to power the selection of synthetic control arms and other innovative designs.  LLMs trained on clinical research data and built using a retrieval-augmented generation (RAG) model can also automatically identify inconsistencies between data sources that might be missed through manual analysis, reducing database lock timelines.  AI can also reduce administrative burden by auto-generating patient narratives and draft reports. 

Increasing the Pace of Development

Speed is another critical factor. The rapid development of COVID-19 vaccines demonstrated that under the right conditions, it is possible to run highly efficient trials. AI can replicate this success by accelerating various aspects of trial execution. For example, AI could automatically draft study protocols, informed consent forms, and other essential documents and structure the key elements to automate data collection forms and database builds.  In a public health emergency, these activities are often done in parallel, so increasing automation is crucial and can save lives. 

Beware the Pitfalls

However, the adoption of AI into clinical trials is not without its challenges. Data privacy is a significant concern, as AI systems require access to large amounts of sensitive patient data. Ensuring that this data is handled securely and ethically is paramount. Other potential issues include ensuring proper regulatory compliance and ensuring a high-quality output from AI systems. Additionally, there is a need for specialized expertise to implement and manage AI systems, which can be a barrier for some organizations. 

Transforming Clinical Research One Step at a Time…

Despite these challenges, the potential benefits of AI in clinical trials are immense. By making trials quicker and more cost-effective, AI can help bring new treatments to market faster and improve healthcare outcomes worldwide. Over the coming weeks, this blog series will explore each of these applications in detail, providing insights into how AI is reshaping the future of clinical trials. We will examine how AI assists in protocol design, patient recruitment, data management, safety monitoring, and more, offering a comprehensive guide to leveraging AI in clinical research. Stay tuned as we embark on this exciting journey into the world of AI-driven clinical trials.

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Noble Shore, VP, Technology Strategy & Product Adoption, is a seasoned expert in digital health and clinical trials, with over 20 years of experience in the pharmaceutical and biotechnology industries. With a strong background in leading cross-functional teams, Noble has driven innovation in patient recruitment, data management, and operational efficiencies. His strategic insights and commitment to advancing clinical research have made significant impacts on the development and delivery of life-saving treatments. Currently, Noble leverages his expertise to enhance digital health initiatives, streamline clinical trial processes, and foster collaborations that accelerate the path to market for new therapies.