TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction
Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate/event, serious adverse event, mortality event, trial approval outcome, trial failure reason, drug dose, and design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.
Authors
- Yue Wang (ORCID: https://orcid.org/0009-0007-5011-2018)
- Tianfan Fu (ORCID: https://orcid.org/0000-0002-5574-2541)
- Mingchen Cai (ORCID: https://orcid.org/0009-0007-3682-5572)
- Jintai Chen (ORCID: https://orcid.org/0000-0002-3199-2597)
- Lucas M. Glass (ORCID: https://orcid.org/0000-0001-6613-5205)
- Yingzhou Lu (ORCID: https://orcid.org/0009-0008-7774-6018)
- Kexin Huang (ORCID: https://orcid.org/0000-0001-6693-8390)
- Yuqiang Li (ORCID: https://orcid.org/0000-0001-6756-6154)
- Jian Wu
- Miao Lin (ORCID: https://orcid.org/0009-0002-8191-7947)
- Jimeng Sun (ORCID: https://orcid.org/0000-0003-1512-6426)
- Marinka Žitnik (ORCID: https://orcid.org/0000-0001-8530-7228)
- Yaojun Hu
- Cao Xiao (ORCID: https://orcid.org/0000-0002-3869-6942)
- Hongxia Xu (ORCID: https://orcid.org/0000-0001-5384-4627)
- Xu Cao (ORCID: https://orcid.org/0000-0002-1262-7244)
Institutions
- Entrust (GB)
- Nanjing Tech University (CN)
- Harvard University (US)
- Zhejiang University of Science and Technology (CN)
- Committee on Institutional Cooperation (US)
- Beijing Academy of Artificial Intelligence (CN)
- IQVIA (United States) (US)
- Zhejiang Lab (CN)
- Guangdong Academy of Medical Sciences (CN)
- Guangdong Provincial People's Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Guangzhou HKUST Fok Ying Tung Research Institute (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Southern Medical University (CN)
- Zhejiang University (CN)
- Nanjing University (CN)
- University of Hong Kong (HK)
- South China University of Technology (CN)
- Stanford University (US)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1038/s41597-025-05680-8
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Nanjing University