Advanced technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have become the cornerstone of modern clinical trial success, incorporating many of the technologies that have transformed clinical development.
In recent years, the health and life sciences industry has changed the game by leaps and bounds to the digital age, with innovations and scientific breakthroughs that are improving patient outcomes and population health. Therefore, embracing digital transformation is no longer an option, but an industry standard. Let’s explore what this really means for clinical development.
An Accelerated Path to Better Outcomes
Over the years, technology has enabled clinical leaders to successfully reduce costs while accelerating the development phase. These technologies facilitate the structuring of complex data environments—a demand created by the exponential growth of data sources containing valuable information for clinical research.
Today, the volume, variety, and velocity of structured and unstructured data generated by clinical trials exceeds traditional data management processes. The reality is that there is too much data from too many sources to be managed by a human team alone. In response to this, in recent years, artificial intelligence/machine learning techniques have been shown to hold great potential for automating data standardization while ensuring quality control, thereby reducing the burden on researchers with minimal human intervention.
Once data is collected and simplified in a single automated ecosystem, clinical trial leaders can begin to benefit from faster, smarter insights driven by the application of machine analytics. These include creating predictive and prescriptive insights that can help researchers and sites discover best practices for future processes. Together, these capabilities can improve research outcomes, patient experience, and safety.
Learn about compliance and privacy
Privacy and compliance must be considered when we consider the use of patient data. The bar for applying any technology to clinical trial execution is high.
Efforts must adhere to Good Clinical Practice (GcP) and validation requirements to ensure that the result is valid as it is predictable and reproducible. Furthermore, how any AI algorithm makes decisions to justify correctness and avoid any potential bias must be transparent and explainable. From a compliance perspective, this has become more important than ever, as regulators consider algorithms part of their basis for approval.
keep h(uman) in healthcare
The goal of implementing AI/ML in clinical research is not to replace humans with digital tools, but to increase their productivity through efficient human augmentation and automation of routine tasks. Until advanced technologies are applied to clinical trials, there is an unmet need for agile methods, where researchers and organizers can focus only on key requirements and delivery of results.
The intelligent application of technology allows humans to interact with artificial intelligence models resulting in better results for research, and even at the most advanced stage, data science techniques will never replace human data scientists. However, it does provide a mutually beneficial environment where workflow enhancements allow data scientists to ease the data burden, while AI models thrive through human feedback. This continuous learning of AI models is called continuous integration/continuous delivery (CI/CD).
The integration of manpower and technology increases efficiency, improves compliance and excellent patient personalization. Furthermore, no matter how efficient the algorithms become, the decision-making power will always belong to humans.
Looking to a bold future
AI/ML strategies are redefining clinical development cycle Unprecedented – As the industry moves into new frontiers, digital transformation is leading to incredible advancements that will revolutionize the space forever. Today’s leaders have the opportunity to apply advanced technologies to solve historically complex problems in the field.
We are already seeing better site selection, more effective risk-based quality management, improved patient monitoring and safety, enhanced patient recruitment and participation, and overall improved research quality—and this is just the beginning.
Photo: Blue Planet Studios, Getty Images
