One of the many consequences of the ongoing pandemic has been a steep decline in participation in an array of clinical trials due to lockdowns and other pandemic-related changes in social behavior.
But in a recent interview with our colleague Bill Siwicki at HealthcareITNews, Jeff Elton, CEO of ConcertAI, an AI solutions provider, described how AI has stepped up to fill the void, a development that is leading to a broader movement toward decentralized clinical trials.
Even as the pandemic put many trials on hold, explained Elton, researchers have been able to continue digging into data already stored in cancer centers by tapping natural language processing and other tools.
“The clinical settings have mountains of data,” he said. “When participation in trials plunged, they had to quickly and efficiently leverage all the data at their fingertips to find as many potential eligible patients. People working manually would have taken too long and might overlook something. AI has been able to do it. AI enhances the ability to identify patients eligible for clinical studies.”
Indeed, he added, “It is critical to understand that if there is no data there is no AI. Meaningful AI and machine learning capabilities require broad data access, the ability to prepare data for specific AI methods and tools, and reserved data for independent validation. Of course, we also must be vigilant of underlying health and biological trends for retraining or re-specification of AI models. We can also generate evidence from complementary data from retrospective sources for prospective studies – and sometimes retrospective data alone for label expansions.”
As he sees the clinical trial landscape, decentralization has been emerging in recent years, but “COVID-19 was the tipping event, or shock, that accelerated the trend. Decentralized trials do not require AI at all, incidentally, but can leverage AI given that workflows are all digital and most data is machine readable. We will enter a period where decentralized trials are at scale, coexisting with legacy approaches.”
The good news, said Elton, is that the change will be good for both treatments and subsequent patient outcomes. “It's good for patients, first and foremost, because they can participate in trials in a broader array of treatment settings. It's good for treatment innovation, because more study alternatives are available in more settings with lower barriers to participation.
Standard-of-care treatment for novel therapeutics versus a separate clinical trial should increase the likelihood of a positive clinical outcome. We want to bring more potentially beneficial options to patients, faster and with greater precision.”