Artificial intelligence (AI)-assisted trial matching is entering clinical research practice through formal tools, patient searches, vendor platforms, and informal use of public AI systems. This plain-English session will help research teams understand how AI may assist trial screening while also creating privacy, documentation, and eligibility risks. Using synthetic metastatic breast cancer patient profiles derived from actively enrolling trials identified through the Aggregate Analysis of ClinicalTrials.gov (AACT) database, attendees will examine common AI trial-matching errors without using real patient data nor protected health information (PHI).
The session will address practical safeguards, including PHI-removal checks, prompt preparation, human review of AI outputs, biomarker interpretation, prior therapy sequencing, missing-data assumptions, and overlooked exclusion criteria. Attendees will leave with coordinator-facing tools for reviewing AI-generated trial matches, documenting uncertainty, escalating eligibility questions, and keeping AI-assisted screening human-supervised, compliant, and appropriately bounded.
CEU: 1.0 ACRP
Speakers:
Joshua Cook, M.S., Data Science; M.S., Clinical Research Management; ACRP-PM, CCRC, Clinical Research Coordinator; Adjunct Instructor, Florida Cancer Specialists & Research Institute
William "Billy" Gonzalez Barnes, A.A., NREMT-B, Student, University of West Florida