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

A related area of intense interest where AI might prove to be effective is in building virtual patients. At the recently concluded World Medical Innovation Forum in Boston, panelists agreed that while it may not be possible for clinical trials to go completely virtual with AI, but offered that advances in AI could replace some subset of patients with virtual modeling, saving both time and money. It must be mentioned here that in many cases we still do not understand how side effects are generated, and we don’t even always understand the mechanism of action in the primary effect. But it may still be possible for AI to allow companies to get drugs approved with less medical evidence in humans, supplementing this by collecting evidence when the drug is on the market, according to Joseph Scheeren, senior advisor for R&D at Bayer. Colin Hill, CEO and cofounder of GNS Healthcare, believes that the future is headed towards virtually simulating drugs down to the molecular level, and using randomized clinical trials to confirm results of virtual experiments.

Another area where AI is likely to replace humans in the near future is data cleanup. Data are messy, and finding meaning amongst the noise and chaos requires a lot of manpower and, consequentially, money. AI can help people clean and analyze data in a smart and automated way—potentially speeding up the clinical trials process. Just cleaning the data after a trial takes one-to-two months, but AI can do it in a day, said Joseph Scheeren, senior advisor for R&D at Bayer. While within a broader context, this example is obviously one that is narrowly defined and most probably related to a very specific clinical trial, but the fact remains that AI has already started showing promises to speeding up data cleanup by a significant factor.

Keeping patients engaged is a huge problem for clinical trials. AI can help retain patients’ interests—allowing for real-time immersion and communication—supporting patient-centric trials. AI can be used to monitor and flag issues with patients’ treatment symptoms and managing medication intakes. Other technologies such as wearables can augment this by allowing patients to share information with researchers in real time, reducing or eliminating the need for patients to travel to sites, increasing patient adherence and compliance. AI can then process this huge real-time data and build intervention plans if a patient is at risk of dropping out of a trial. Reducing the frequency and length of clinical visits can lead to lower site costs and improvement in the quality of patient experience. It is also conceivable that once a model is established, it will be feasible to predict an individual participant’s or patient’s behavior without need for a large quantity of related data.

Disseminating outcomes of clinical trials is a big unresolved problem—a patchwork of confusion and wasted efforts—often leading to trials being repeated at huge costs to innovative companies trying to market their drugs or devices or treatments to patients who cannot wait. AI can help in this regard. King’s College London ran a machine learning project called Robot Reviewer, funded by the Medical Research Council (MRC) and others. The aim of this project was to develop a system that will automate bias assessment in systematic reviews. These syntheses will enable decision makers to consider the entirety of the relevant published evidence. The hope is that this will lead to a better overall meta-analysis for finding new drugs and treatments.

These are some of the exciting efforts in the AI realm that have the potential for streamlining the future of clinical trials. In the next segment of the blog post, we will explore some specific examples of these advancements and how they can be used to improve trial outcomes. 

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