How AI and ML can transform clinical research

clinical trials AI

By Gary Shorter, head of artificial intelligence at IQVIA

Clinical trials are currently in an era of transformation through digitization, with much of the change being attributed to the integration of technologies such as natural language processing (NLP), artificial intelligence (AI), and machine learning (ML).

Like many industries, health and life sciences are experiencing rapid acceleration of the digital evolution, bringing about revolutionary innovations and groundbreaking discoveries that have improved patient outcomes and population health, with great promise for future growth. Yet, alongside these breakthroughs are an abundance of new challenges, such as new technologies and new data sources demanding greater regulatory standards and processes. 

To keep ahead of the increasing regulatory activity, the industry must embrace automation – but how does this truly impact the clinical trial landscape?

AI allows greater best practices for future clinical trials

In the face of increased challenges over recent years, clinical innovators and industry leaders have fully grasped that the implementation of technology can accelerate research and development while effectively reducing costs.

The arrival of the pandemic resulted in an exponential increase in volume, variety and velocity of data with a plethora of new data sources containing valuable information for clinical research being added to enable decentralized clinical trials (DCT). To properly process and structure this complex data, clinical technologies have become essential. Looking within clinical trials, the previously established data management systems are becoming overwhelmed by the complexity and massive quantities of structured and unstructured data produced. As we continue along this path of mass data creation, from manifold sources, human teams will need to work alongside advanced technology.

AI/ML technologies are bringing significant value to the table through the ability to maintain data quality control while automating standardization. By integrating AI/ML, the workload placed on human teams can be drastically reduced. Once the data collection has been streamlined and contained within one network, the benefits of machine-driven insights can be fully actualized, demonstrating more swift, intelligent, and holistic results. The continuous learning gained from the resulting predictive and prescriptive insights leads to uncovering greater best practices for future trials. These capabilities, on the whole, increase research’s safety, patient experience, and outcomes.

Emphasizing privacy and compliance

The healthcare industry is riddled with strict compliance and privacy standards, which fully extends to patient data in clinical trials. Introducing technology such as AI into clinical trials demands that these standards are being met. There is a rigor that must be adhered to.

Good clinical practice (GCP) outlines the requirements for clinical trial activities and processes, combined with validation requirements to ensure that the trial is predictable and repeatable. At the same time, it is critical that there be available and accessible explanation and transparency surrounding the use of AI algorithms to provide accurate, impartial decisions. From a compliance standpoint, these requirements are becoming more crucial than ever, as algorithms are observed by regulators to inform, in part, the basis for product approvals.

A collaborative relationship

Despite the continued advances of AI/ML, these technologies will never fully substitute the capabilities of human data scientists. The use of AI/ML in clinical clinical trials is intended as a partnership between humans and intelligent technology, rather than a total replacement. The implementation of human augmentation and automation enables agile methods that allow researchers and organizers to focus their efforts on results delivery while mitigating the burden of critical but highly involved tasks.

Meanwhile, the AI models benefit and thrive from human feedback—a process known as continuous integration/continuous delivery (CI/CD).

This collaboration between advanced technologies and human capabilities ultimately results in improved patient personalization, efficiency, and compliance across the board. All the while continuing to leave the power of decision-making in the hands of humans.

The tip of the iceberg

The digital transformation of clinical trials, largely impacted by AI/ML integrations, has only begun to spur advancements that will continue to transform the industry indefinitely. Leaders in the field are now presented with the opportunity to remedy problems with advanced technologies, that would otherwise pose great challenges.

Since the industry’s embarkation on a digital metamorphosis, we’ve witnessed improved patient monitoring and safety, increased effectiveness in risk-based quality management, bettered site selection, enhanced patient recruitment and engagement, and sweeping improvement of study quality and operational efficiencies. Just at the beginning of this journey, clinical trials have many more benefits to encounter along the route of digital evolution.

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