Using Artificial Intelligence to Manage Clinical Trial Conduct

In a recent video interview with Applied Clinical Trials, Jeff Sidell, PhD, chief technology officer, Advarra, discussed current challenges in clinical research technology. He highlighted the overabundance of systems and emphasized the need for integration to streamline processes and reduce administrative overhead. Sidell also discussed the potential of AI, particularly predictive analytics, to automate repetitive tasks, enhancing efficiency and quality.

A transcript of Sidell’s conversation with ACT can be found below.

ACT: What are some unique areas of clinical research that could benefit from the use of artificial intelligence (AI)?

Sidell: I know that there's been a lot of attention paid to AI in the actual research cycle, that's not what Advarra is focused on. We're focused on the conduct of the clinical trials, all the way from submission to the IRB (institutional review board) through the execution of the trial and oversight by the sponsors of the sites and the subjects. If you think about it, that whole process is a series of smaller processes. It's people doing things somewhat repetitively. Clinical trials are not a factory, they're not banging out motor cars, but it is nonetheless repetitive processes, or similar processes. That's an area where artificial intelligence, particularly predictive analytics, can come in and automate, or semi-automate the process, and the efficiency gains effectiveness. A lot of people think you're going to make it faster, and my job is going to go away, or something like that. It does make the process more efficient. It allows people to do more with the same number of resources, but it also will typically increase the quality of the work that's done and it's not that the machine learning models don't make mistakes, but they are incredibly dependable—if it's been trained correctly, if the model is a good model—then they will repeatedly do what they were trained to do over and over and over and they'll do it well. They don't have bad days. They don't come in sick or hungover or something like that. They do have to be kept up to date, though. There is a maintenance aspect to introducing machine learning models into processes, but that's where I see the greatest, ripe for opportunity area, because it is such a process-heavy industry. Those processes can be made much, much, much more efficient.