整合临床试验库与科研文献，让临床研究数据更易获取。
ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access
- 用大模型自动从论文提取试验信息，补全注册库数据
- 数据可访问性提升83.8%，显著超越单一数据源
- 适合医生、研究人员和政策制定者快速查证证据
我们提出ClinicalTrialsHub，一个以交互式搜索为核心的平台，整合了ClinicalTrials.gov全部数据，并通过自动提取和结构化PubMed科研文章中的试验相关信息进行增强。相比仅依赖ClinicalTrials.gov，该系统使结构化临床试验数据的可访问性提升了83.8%，有望为患者、临床医生、研究人员及政策制定者提供更便捷的证据获取途径，推动循证医学发展。平台采用GPT-5.1和Gemini-3-Pro等大语言模型，实现对全文论文的自动解析，提取结构化试验信息；将用户查询转化为结构化数据库检索，并提供带来源标注的问答系统，生成基于证据的答案并关联具体原文句子。我们通过涉及临床医生、临床研究人员及药学与护理学博士生的用户研究，以及对其信息抽取与问答能力的系统性自动评估，验证了平台的有效性。
原文摘要 · Abstract (English)
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.
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