MatchMiner-AI: An Open-Source Solution for Cancer Clinical Trial Matching
To address the critical challenges of insufficient patient recruitment in oncology clinical trials and low efficiency of manual eligibility screening, this paper proposes TrialSpace: an interpretable, AI-assisted matching framework grounded in semantic embeddings. Methodologically, it introduces a novel decoupled matching paradigm—jointly modeling patient electronic health records and trial protocols within a disease-specific semantic space. Clinical BERT is fine-tuned to extract domain-aware features, followed by vector-based candidate retrieval and a lightweight binary classifier for eligibility verification. Key contributions include: (1) the first open-source clinical trial matching toolkit and a synthetically generated benchmark dataset; (2) strong performance (Top-5 recall >92% on synthetic data), model interpretability, and clinical deployability; and (3) a publicly available interactive demo, along with full source code and pre-trained model weights. TrialSpace significantly enhances physician screening efficiency while supporting evidence-based decision-making—not replacing clinicians, but augmenting their expertise.