@inproceedings{park-etal-2026-clinicaltrialshub,
title = "{C}linical{T}rials{H}ub: Bridging Registries and Literature for Comprehensive Clinical Trial Access",
author = "Park, Jiwoo and
Liu, Ruoqi and
Jagdale, Avani and
Srisuwananukorn, Andrew and
Zhao, Jing and
Zhang, Ping and
Kumar, Sachin",
editor = "Croce, Danilo and
Leidner, Jochen and
Moosavi, Nafise Sadat",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-demo.26/",
pages = "359--396",
ISBN = "979-8-89176-382-1",
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."
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%0 Conference Proceedings
%T ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access
%A Park, Jiwoo
%A Liu, Ruoqi
%A Jagdale, Avani
%A Srisuwananukorn, Andrew
%A Zhao, Jing
%A Zhang, Ping
%A Kumar, Sachin
%Y Croce, Danilo
%Y Leidner, Jochen
%Y Moosavi, Nafise Sadat
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-382-1
%F park-etal-2026-clinicaltrialshub
%X 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.
%U https://aclanthology.org/2026.eacl-demo.26/
%P 359-396
Markdown (Informal)
[ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access](https://aclanthology.org/2026.eacl-demo.26/) (Park et al., EACL 2026)
ACL