ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access

TL;DR

ClinicalTrialsHub is a platform that integrates clinical trial data from registries and literature, using advanced language models to improve access and evidence-based decision-making in healthcare.

Contribution

It introduces a novel system that automatically extracts and structures trial information from literature and integrates it with registry data, significantly enhancing data accessibility.

Findings

Increases access to structured trial data by 83.8%.

Demonstrates effective use of large language models for information extraction.

Validated through user study and automatic evaluation.

Abstract

We present ClinicalTrialsHub, an interactive search-focused platform that consolidates all data from ClinicalTrials.gov and augments it by automatically extracting and structuring trial-relevant information from PubMed research articles. Our system effectively increases access to structured clinical trial data by 83.8% compared to relying on ClinicalTrials.gov alone, with potential to make access easier for patients, clinicians, researchers, and policymakers, advancing evidence-based medicine. ClinicalTrialsHub uses large language models such as GPT-5.1 and Gemini-3-Pro to enhance accessibility. The platform automatically parses full-text research articles to extract structured trial information, translates user queries into structured database searches, and provides an attributed question-answering system that generates evidence-grounded answers linked to specific source sentences. We demonstrate its utility through a user study involving clinicians, clinical researchers, and PhD students of pharmaceutical sciences and nursing, and a systematic automatic evaluation of its information extraction and question answering capabilities.