  Clinical trials are fundamental in developing new drugs, medical devices, and
treatments. However, they are often time-consuming and have low success rates.
Although there have been initial attempts to create large language models
(LLMs) for clinical trial design and patient-trial matching, these models
remain task-specific and not adaptable to diverse clinical trial tasks. To
address this challenge, we propose a clinical trial foundation model named
Panacea, designed to handle multiple tasks, including trial search, trial
summarization, trial design, and patient-trial matching. We also assemble a
large-scale dataset, named TrialAlign, of 793,279 trial documents and 1,113,207
trial-related scientific papers, to infuse clinical knowledge into the model by
pre-training. We further curate TrialInstruct, which has 200,866 of instruction
data for fine-tuning. These resources enable Panacea to be widely applicable
for a range of clinical trial tasks based on user requirements.
  We evaluated Panacea on a new benchmark, named TrialPanorama, which covers
eight clinical trial tasks. Our method performed the best on seven of the eight
tasks compared to six cutting-edge generic or medicine-specific LLMs.
Specifically, Panacea showed great potential to collaborate with human experts
in crafting the design of eligibility criteria, study arms, and outcome
measures, in multi-round conversations. In addition, Panacea achieved 14.42%
improvement in patient-trial matching, 41.78% to 52.02% improvement in trial
search, and consistently ranked at the top for five aspects of trial
summarization. Our approach demonstrates the effectiveness of Panacea in
clinical trials and establishes a comprehensive resource, including training
data, model, and benchmark, for developing clinical trial foundation models,
paving the path for AI-based clinical trial development.
