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

• URL: http://arxiv.org/abs/2505.08508v1
• Date: Tue, 13 May 2025 12:39:06 GMT
• Title: TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching
• Authors: Majd Abdallah, Sigve Nakken, Mariska Bierkens, Johanna Galvis, Alexis Groppi, Slim Karkar, Lana Meiqari, Maria Alexandra Rujano, Steve Canham, Rodrigo Dienstmann, Remond Fijneman, Eivind Hovig, Gerrit Meijer, Macha Nikolski,
• Abstract summary: We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching.<n>Built on fine-tuned, open-source large language models, TrialMatchAI ensures transparency and maintains a lightweight deployment footprint.<n>In real-world validation, 92 percent of oncology patients had at least one relevant trial retrieved within the top 20 recommendations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Patient recruitment remains a major bottleneck in clinical trials, calling for scalable and automated solutions. We present TrialMatchAI, an AI-powered recommendation system that automates patient-to-trial matching by processing heterogeneous clinical data, including structured records and unstructured physician notes. Built on fine-tuned, open-source large language models (LLMs) within a retrieval-augmented generation framework, TrialMatchAI ensures transparency and reproducibility and maintains a lightweight deployment footprint suitable for clinical environments. The system normalizes biomedical entities, retrieves relevant trials using a hybrid search strategy combining lexical and semantic similarity, re-ranks results, and performs criterion-level eligibility assessments using medical Chain-of-Thought reasoning. This pipeline delivers explainable outputs with traceable decision rationales. In real-world validation, 92 percent of oncology patients had at least one relevant trial retrieved within the top 20 recommendations. Evaluation across synthetic and real clinical datasets confirmed state-of-the-art performance, with expert assessment validating over 90 percent accuracy in criterion-level eligibility classification, particularly excelling in biomarker-driven matches. Designed for modularity and privacy, TrialMatchAI supports Phenopackets-standardized data, enables secure local deployment, and allows seamless replacement of LLM components as more advanced models emerge. By enhancing efficiency and interpretability and offering lightweight, open-source deployment, TrialMatchAI provides a scalable solution for AI-driven clinical trial matching in precision medicine.

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