Year: 2026
Type: conference-paper
Abstract: Matching patients to clinical trials is a critical bottleneck hindered by complex eligibility criteria.While conversational AI offers a promising solution, its safe deployment depends on high-quality, domain specific data.This paper introduces three benchmark datasets designed to support the development and evaluation of conversational agents for clinical trial pre-screening.First, a manually-annotated paired-criterion dataset provides a gold standard for structuring raw criteria, which we used to objectively group 12,596 criteria.Second, we curated a human-authored question benchmark to validate the clinical fidelity and patient-centric clarity of questions generated by a medical LLM, ensuring the AI's dialogue is accurate and understandable.Third, we constructed a human-validated assessment corpus of criterion-question-answer tuples with human-labeled outcomes to evaluate criterion classification based on a patient's answer to a generated question.The primary contribution of this work is a foundational set of benchmark datasets, designed to support and evaluate key components for a chatbot for clinical trial search.
Authors: Yumeng Yang, Ethan Ludmir, Kirk Roberts
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7166532075