At last year’s Healthcare Information and Management Systems Society conference (HIMSS 2018), Mayo Clinic announced that it increased enrollment by 80% by using IBM’s Watson AI platform for breast cancer. Dr. Kyu Rhee, chief health officer at Watson Heath, further stated, “Watson for clinical trial matching understands key patient attributes and how to identify them in a variety of formats…to effectively evaluate a patient against the inclusion or exclusion criteria for a trial.” This is clearly a very specific example related to a very particular type of trial, using a commercial closed platform, and with no guarantee that the results can be repeated in other trials with different patient’s requirements and sample size restrictions. Yet, the idea that AI can be used to increase enrollment is very exciting. Enrollment has always been a challenge in clinical trials, and if AI is effective in making an impact there, it compels us to ask: where else can AI be used to improve clinical trials?
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.”