Revolutionizing the Clinical Research Ecosystem with AI & ML 


Revolutionizing the Clinical Research Ecosystem with AI & ML 

Gary Shorter

By Gary Shorter, Head of Artificial Intelligence at IQVIA

As clinical trials advance, technologies like natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) have become a core part of successful execution. Integrated into many of the technologies used by clinical trials, NLP, and AI/ML enable the transformation of clinical development.

The rapid digital acceleration of the health and life sciences industry over the past few years has presented incredible innovations and breakthroughs that improve patient outcomes and population health and hold great potential to continue growth in those areas.

However, alongside those breakthroughs come a lot of new technology and new data sources as well as higher levels of regulatory rigor concerning how software feeds and interprets data into insights.

Therefore, adopting digital transformation is now an industry standard for success – but what precisely does that mean for the clinical development landscape?

Improving Efficiency with Automation

Driven by the many new challenges of recent years, the ability of technology to accelerate stages of research and development while driving down operating costs has been fully realized by the industry. The pandemic has of course played a role in this, but the expansion of existing data sources containing valuable information for clinical studies and advent of all new ones, such as social media, has rapidly escalated. To keep pace, clinical technologies have become the bedrock of structuring complex data environments.

Currently, even as the industry has ramped up traditional management processes, they’re no match for the quantity, array, and celerity of structured and unstructured data generated by clinical trials. To put it plainly, human teams alone cannot handle this sea of data, which is only set to grow further. That’s where AI/ML technologies come in, as proven to hold great potential for automating data standardization while maintaining quality control, therefore also lightening the load of manual data management that researchers currently face.

By compiling global data ingestion into a more standard automated ecosystem, clinical leaders can begin to reap the benefits of machine-driven insights, which are both more quickly extracted as well as more intelligent and holistic. The formation of predictive and prescriptive analytics via these insights enables continuous learning that will inform the creation of new best practices for the future of the space. These capabilities provide the overarching improvement of research outcomes, patient experience, and safety.

Privacy and Compliance as a Priority

High standards for privacy and compliance must be top of mind when discussing patient data, with no exception when it comes to embracing technology. Clinical research processes and activities must meet the requirements in place as outlined by Good Clinical Practice (GcP), as well as validation requirements to ensure the study is predictable and repeatable. In tandem, there must be transparency and accessible explanation pertaining to the use of AI algorithms to provide correct, unbiased decisions. More than ever, these rigorous conditions can become deciding factors between compliance and non-compliance, as regulators analyze the algorithms as part of their basis for approvals.

Introducing Tech to Enhance Human-Driven Trials

AI/ML implementations are not meant to replace humans, but to enhance and augment their productivity and efficiency throughout the course of a trial. Advanced technologies integrated into clinical trials filled the gap in the industry for an agile procedure that allowed researchers and organizers to undividedly focus on vital requirements and the delivery of results.

While intelligent collaboration between AI/ML and humans brings better research outcomes, even incredibly advanced data science technology could never replace the human data scientist. However, a mutually beneficial relationship is struck, as the technology provides advanced human augmentation and automation of monotonous labor, easing the efforts of the data scientists and clinical researchers, while AI models improve with each bit of human feedback. This continuous learning and development by an AI model is referred to as Continuous Integration/Continuous Delivery (CI/CD).

The amalgamation of human and technological capabilities results in increased efficiency, enhanced compliance, and exceptional patient personalization. Yet, regardless of technological advancements, the decision-making power will continue to remain in the hands of humans.

Just the Beginning

Digital transformation, as heavily influenced by AI/ML integrations, is redefining clinical development in ways we’ve only begun to see, and it is leading the way to advancements that will transform the industry indefinitely. Clinical trial leaders are now posed with the opportunity to resolve historically challenging problems using advanced technologies. Since embracing digital evolution, we’ve seen increased effectiveness in risk-based quality management, improved patient recruitment and engagement, enhanced patient monitoring and safety, improved site selection, and better overall study quality and operational efficiencies – and that’s just the tip of the iceberg.