ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910059843289088 |
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| author | Park, Jiwoo Liu, Ruoqi Jagdale, Avani Srisuwananukorn, Andrew Zhao, Jing Li, Lang Zhang, Ping Kumar, Sachin |
| author_facet | Park, Jiwoo Liu, Ruoqi Jagdale, Avani Srisuwananukorn, Andrew Zhao, Jing Li, Lang Zhang, Ping Kumar, Sachin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08193 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access Park, Jiwoo Liu, Ruoqi Jagdale, Avani Srisuwananukorn, Andrew Zhao, Jing Li, Lang Zhang, Ping Kumar, Sachin Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval 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. |
| title | ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Information Retrieval |
| url | https://arxiv.org/abs/2512.08193 |