This research introduces a novel multimodal approach that leverages large language models to predict clinical trial outcomes, potentially reducing the years-long, costly drug development process.

• Integrates diverse data sources (text, molecular structures, and images) into a unified prediction framework
• Outperforms traditional deep learning methods with more accurate trial outcome predictions
• Demonstrates how LLMs can process complex, multimodal medical data without specialized encoders
• Provides a scalable solution that could significantly reduce pharmaceutical R&D costs

For healthcare organizations and pharmaceutical companies, this technology could transform drug development by identifying promising candidates earlier and reducing investment in likely-to-fail trials, ultimately accelerating innovation and reducing healthcare costs.

Multimodal Clinical Trial Outcome Prediction with Large Language Models