{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(1)"],"submitter":["Chen J"],"pubmed_abstract":["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, tri"],"journal":["Scientific data"],"pagination":["1564"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12475113"],"repository":["biostudies-literature"],"pubmed_title":["TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction."],"pmcid":["PMC12475113"],"pubmed_authors":["Wu J","Fu T","Hu Y","Chen J","Cai M","Lu Y","Huang K","Xu H","Zitnik M","Li Y","Cao X","Glass L","Wang Y","Lin M","Sun J"],"additional_accession":[]},"is_claimable":false,"name":"TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.","description":"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, tri","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-14T05:02:13.215Z","creation":"2026-06-14T03:08:20.464Z"},"accession":"S-EPMC12475113","cross_references":{"pubmed":["41006354"],"doi":["10.1038/s41597-025-05680-8"]}}