Where can AI impact the clinical trials process directly? Application areas include increasing trial efficiency through better protocol design, patient enrollment and retention, and study start-up, which were each named as prime candidates for improvement by nearly 40% of sponsors in a recent ICON-Pharma Intelligence survey. With clinical trials accounting for 40% of pharma research budgets, sponsors need new ways to accelerate timelines and reduce costs. Joseph Scheeren, senior advisor for R&D at Bayer, estimates that AI can cut 30-40% of the time required for a clinical trial: “In R&D, speed is everything.”


AI platforms can help optimize study design in clinical trials by mining thousands of related source documents for key actionable information. Study design focuses on trials intended to provide primary evidence of safety and efficacy (“pivotal” trials) and on regulations to permit substantial flexibility (“adequate and well-controlled trials”). Selecting a study design depends on the stated objectives, expectations, and practicalities, such as available population, site requirements, and the potential impact on medical practices.

“AI can simulate the entire control arm of a clinical trial using a previously collected dataset,” says Dr. Amy Abernethy, Chief Medical and Scientific Officer at Flatiron Health, a New York-based company purchased by Roche for $1.9 billion in early 2018. This is a very ambitious goal, and AI may not yet be ready for that to happen dependably, but the fact remains that study design is still a very human-driven process for obvious reasons. Presently, AI can help by aiding in the more labor-intensive part of this process. For example, it can help by mining source documents within the healthcare domain, finding answers to questions that need answering, providing added scope, depth, and breath to the research before a study is designed.

AI can help sponsors and sites to connect, collaborate, and begin conducting trials. AI facilitates enabling sites to create their own accounts and upload their personal data for populating information in study startup documents and speeding up different feasibility processes. Trials can be streamlined by helping study sites and the sponsor maintain awareness of who needs to do what, when, and track milestones, due dates, timelines, as well as any protocol deviations, adverse events, and other events of interest. AI can also learn from prior forms, documents, contracts, and budgets to tailor new materials for each site.

AI is being used actively to process and understand trial-related documents using NLP. Similarities and differences between different parts of the document are identified and used to build a database of reference text that can be employed in writing future protocols. On contract matters, AI can help identify and extract paragraphs or articles that address similar issues, such as liability and insurance. By identifying similar documents or parts of a document, the sponsor can quickly identify what parts of the document are common and what parts of a document need special attention e.g., legal jurisdictions.     

AI can help select subjects who have a greater likelihood of passing screening and a greater propensity to remain in and complete a study. “Worse than a site that doesn’t enroll patients is a site that enrolls a lot of patients and those patients don’t stay in the study,” said Dr. Stephen Wiviott, executive director of the Clinical Trials Office at Partners HealthCare. Building a patient profile based on reported data using machine learning and other predictive analytics techniques can help in this respect. Wiviott estimates that costs of conducting a trial from beginning to end can be cut by 90 percent if AI is applied throughout the clinical trial process, adding, “So much of this money is spent on humans checking other humans’ work.” Whether or not this level of improvement can be obtained today, it is likely that there is great promise of cost reduction for a typical clinical trial by using AI appropriately.

It is easy to extrapolate a trend from the examples provided above. AI can be a very effective tool in cutting through the labor-intensive, manually exhaustive, and often mind-boggling tedious work related to planning, designing, and executing clinical trials. But this is just a start. In the next segment of this blog post, we will explore some more advanced application of AI within the context of building a more robust clinical trial.  

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