  Clinical trials are pivotal for developing new medical treatments, yet they
typically pose some risks such as patient mortality, adverse events, and
enrollment failure that waste immense efforts spanning over a decade. Applying
artificial intelligence (AI) to forecast or simulate 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 and a deep understanding of trial designs have hindered the
involvement of AI thus far. This paper tackles these challenges by presenting a
comprehensive suite of meticulously curated AIready datasets covering
multi-modal data (e.g., drug molecule, disease code, text,
categorical/numerical 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. The curated dataset, metrics, and
basic models are publicly available at
https://github.com/ML2Health/ML2ClinicalTrials/tree/main/AI4Trial.
