TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching
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| Format: | Preprint |
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2025
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| author | Abdallah, Majd Nakken, Sigve Bierkens, Mariska Galvis, Johanna Groppi, Alexis Karkar, Slim Meiqari, Lana Rujano, Maria Alexandra Canham, Steve Dienstmann, Rodrigo Fijneman, Remond Hovig, Eivind Meijer, Gerrit Nikolski, Macha |
| author_facet | Abdallah, Majd Nakken, Sigve Bierkens, Mariska Galvis, Johanna Groppi, Alexis Karkar, Slim Meiqari, Lana Rujano, Maria Alexandra Canham, Steve Dienstmann, Rodrigo Fijneman, Remond Hovig, Eivind Meijer, Gerrit Nikolski, Macha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08508 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching Abdallah, Majd Nakken, Sigve Bierkens, Mariska Galvis, Johanna Groppi, Alexis Karkar, Slim Meiqari, Lana Rujano, Maria Alexandra Canham, Steve Dienstmann, Rodrigo Fijneman, Remond Hovig, Eivind Meijer, Gerrit Nikolski, Macha Artificial Intelligence Machine Learning Quantitative Methods 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. |
| title | TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching |
| topic | Artificial Intelligence Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2505.08508 |