TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction

Area of Science:

• Biomedical Informatics
• Clinical Trial Management
• Artificial Intelligence in Medicine

Background:

• Clinical trials are essential for medical advancements but face significant risks like patient mortality and enrollment failure, leading to wasted resources.
• The integration of artificial intelligence (AI) in clinical trials can offer predictive insights for design optimization, but has been limited by data complexity and the need for medical expertise.
• Existing challenges in clinical trial design necessitate novel approaches to mitigate risks and improve efficiency.

Purpose of the Study:

• To address the limitations in applying AI to clinical trial design by creating accessible, AI-ready datasets.
• To facilitate the prediction of key clinical trial outcomes and design parameters using AI.
• To accelerate the development of AI-driven tools for optimizing clinical trial design and execution.

Main Methods:

• Curated a comprehensive suite of 23 multi-modal, AI-ready datasets.
• Covered 8 critical prediction challenges in clinical trial design, including duration, dropout rates, adverse events, and approval outcomes.
• Included basic validation methods for each dataset to ensure usability and reliability.

Main Results:

• Developed 23 meticulously curated datasets suitable for AI model training.
• Enabled prediction for crucial aspects of clinical trials such as duration, patient dropout, serious adverse events, mortality, approval outcomes, failure reasons, drug dosage, and eligibility criteria.
• Provided validated datasets to support AI-driven clinical trial design.

Conclusions:

• The release of these open-access datasets is expected to stimulate the development of advanced AI methodologies for clinical trial design.
• This initiative aims to enhance the efficiency and success rates of clinical trials.
• The availability of these resources will contribute to accelerating the development and delivery of medical solutions.