Reducing Cognitive Load: Agentic AI for Efficient Clinical Trial Matching

Clinical trial matching can be a time-intensive process for oncology teams, requiring review of patient histories, treatment timelines, biomarkers, and complex eligibility criteria. In an interview with Targeted Oncology^TM, Shaalan Beg, MD, MBA, FASCO, of ConcertAI, discusses how agentic artificial intelligence (AI) could help streamline this process while reducing the cognitive burden on clinicians and clinical trial coordinators.

Agentic AI can analyze information within the electronic medical record and help identify patients who may be appropriate for clinical trials based on where they are in their treatment journey—not simply their diagnosis or biomarker status. This approach may help oncology teams distinguish between patients who could become eligible in the future and those who may be candidates for a trial now.

Beg also discusses how these tools can be integrated into existing workflows rather than requiring clinicians to navigate another standalone system. By surfacing relevant patient information, potential care gaps, and trial opportunities at the appropriate time, AI may help teams spend less time searching through records and more time making clinical decisions.

For community oncology practices, where efficiency and resource utilization are critical, these capabilities could offer a way to support clinical trial enrollment while minimizing additional administrative demands.