In the aftermath of the Covid-19 pandemic, a new surge in artificial intelligence (AI) technology is beginning to jolt the pharma industry once again. At the Outsourcing in Clinical Trials East Coast conference, the message was clear: trial sponsors and service providers must embrace the change, or risk becoming obsolete.
During a provocative keynote address, Craig Lipset, advisor, and founder of Clinical Innovation Partners, said that no stakeholder has “earned the right” to be involved in clinical trials. Instead, each sponsor and service provider must continually adapt to ensure they are providing value in a rapidly evolving landscape. “We are all at risk of becoming obsolete,” he said.
Throughout the remainder of the first day of the conference, multiple speakers stressed the need to incorporate AI into clinical trials as a tool for adding value in a tough economic climate.
Planning studies with AI
Venkat Sethuraman, Head of Global Biometrics and Data Sciences at Bristol Myers Squibb (BMS), presented on the ways his company was leveraging AI to aid in study planning. BMS is exploring the use of large language models like ChatGPT to develop initial drafts of study protocols, which are then reviewed and edited by human experts. Meanwhile, Sethuraman expects more pharma sponsors to use AI machine learning for remote monitoring patients in real-world settings and developing digital disease biomarkers.
Overall, Sethuraman said he was encouraged by the uptick in regulatory guidance and acceptance of new AI technologies. New data sharing strategies—such as federated learning models—are helping sponsors utilise big data while meeting increasingly strict data compliance regulations.
That said, Setharaman noted that AI technologies can pose concerns for trial access and inclusion, which he called the “digital divide.” There are socioeconomic divisions in which patients can effectively use smartphones and evidence of racial biases in several AI-based tools, such as dermatology imaging software.
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AI and pharma site selection
During a later session on trial site selection, ICON’s Travis Caudill discussed the potential—and limitations—of AI technologies. New AI tools can help predict which investigators are most likely to recruit patients for a trial specific protocol, but there will always be the need for a human element to pair data-driven insights with real-world experience, he explained.
Above all, Caudill stressed the need to find an appropriate middle ground when it comes to utilising big data and AI. Too little data could risk missing important insights, but too much data could drown out more subtle, yet important, trends.
“We now live in a world where there is more data than ever to help with site selection,” Caudill said. “But that can feel overwhelming, and it’s often tough to know where to start.”