TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching

This repository contains the supplementary materials for the paper titled "TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching." It includes: Synthetic patient dataset used for evaluation (Ideal Candidates) Fine-tuned Large Language Models (LLMs) for tasks: named entity recognition (NER), trial re-ranking, and Chain-of-Thought (CoT) reasoning Training and fine-tuning data used to develop the models Matching results on synthetic patient profiles (Ideal Candidates + TREC 2021 & 2022) Normalization dictionaries used for standardizing the extracted biomedical entities These resources are provided to facilitate reproducibility and further research on AI-driven clinical trial matching systems.