{"ID":2830984,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.08193","arxiv_id":"2512.08193","title":"ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access","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.","short_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%...","url_abs":"https://arxiv.org/abs/2512.08193","url_pdf":"https://arxiv.org/pdf/2512.08193v2","authors":"[\"Jiwoo Park\",\"Ruoqi Liu\",\"Avani Jagdale\",\"Andrew Srisuwananukorn\",\"Jing Zhao\",\"Lang Li\",\"Ping Zhang\",\"Sachin Kumar\"]","published":"2025-12-09T02:52:06Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.HC\",\"cs.IR\"]","methods":"[\"Language Model\"]","has_code":false}