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.

翻译：我们提出了ClinicalTrialsHub，一个以交互式搜索为核心的平台，该平台整合了ClinicalTrials.gov的所有数据，并通过自动提取和结构化处理PubMed研究文章中的试验相关信息对其进行扩充。与仅依赖ClinicalTrials.gov相比，本系统将结构化临床试验数据的可访问性提高了83.8%，有望让患者、临床医生、研究人员和政策制定者更便捷地获取信息，从而推动循证医学的发展。ClinicalTrialsHub利用GPT-5.1和Gemini-3-Pro等大型语言模型来增强可访问性。该平台自动解析全文研究文章以提取结构化的试验信息，将用户查询转换为结构化数据库搜索，并提供带归属来源的问答系统，该系统可生成基于证据的答案并关联到特定源语句。我们通过一项涉及临床医生、临床研究人员以及药学与护理学博士研究生的用户研究，以及对其信息提取和问答能力的系统自动评估，展示了其实用性。