Conversational agent improves querying clinical trial information
ClinAgent: A ReAct-Based Agent for Conversational Access to Clinical Trial Information
Artificial IntelligenceComputation and LanguageInformation Retrieval
Summary
Finding detailed information about clinical trials is often hard because the data is scattered and technical. The authors created ClinAgent, a system that lets users ask questions in plain language and get clear, up-to-date answers about clinical trials. It uses an advanced AI approach that combines reasoning with tools to search databases like ClinicalTrials.gov and PubMed. This makes it easier for doctors and researchers to find and understand clinical trial data through a conversation-like interface.
What this means in practice
- •For medical informatics teams: Integrate conversational querying into clinical trial databases to help healthcare professionals find relevant trial information more easily.
- •For biomedical software developers: Develop conversational AI tools that combine multiple data sources for user-friendly access to biomedical research data beyond clinical trials.
Authors
Antonino Vaccarella, Riccardo Cantini, Domenico Talia, Paolo Trunfio, Marianna Talia, Rosamaria Lappano, Marcello Maggiolini
Abstract
Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data without support for natural language interaction or cross-source synthesis. To address this, we introduce ClinAgent, a conversational system based on agentic Retrieval-Augmented Generation (RAG) that enables clinicians and researchers to query clinical trial information in plain language and receive grounded, up-to-date responses across multi-turn interactions. The system centers on a Large Language Model (LLM) agent following the ReAct paradigm, which iteratively reasons over queries, selects among a set of integrated tools, and refines its actions based on intermediate outputs. These tools include a ClinicalTrials.gov search interface, a PubMed module, and a Python-based analyzer operating on a locally cached structured dataset of clinical trials. We evaluate the system using a three-phase framework assessing operational effectiveness, planning quality, tool-use efficiency, and expert qualitative judgments, comparing three LLM backends: Gemini 3.0 Flash and two variants of DeepSeek V3.2 (thinking and non-thinking). Results reveal complementary strengths, with DeepSeek (thinking mode) excelling in planning quality, while Gemini achieves the highest overall performance and strongest expert ratings. Overall, our findings highlight the potential of agentic AI systems to improve the accessibility and synthesis of clinical trial information, supporting more efficient and user-centered biomedical research workflows.