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, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, 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.
@article{arxiv.2407.00631,
title = {TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets},
author = {Jintai Chen and Yaojun Hu and Mingchen Cai and Yingzhou Lu and Yue Wang and Xu Cao and Miao Lin and Hongxia Xu and Jian Wu and Cao Xiao and Jimeng Sun and Yuqiang Li and Lucas Glass and Kexin Huang and Marinka Zitnik and Tianfan Fu},
journal= {arXiv preprint arXiv:2407.00631},
year = {2025}
}