SatIR: New AI System Improves Clinical Trial Matching Using Constraint-Based Retrieval
Researchers have proposed SatIR, a constraint-satisfaction-based retrieval system designed to match patients with eligible clinical trials more accurately than existing methods. The system integrates Satisfiability Modulo Theories (SMT), relational algebra, medical ontology grounding, and large language models to convert complex eligibility criteria into executable formal constraints. On benchmark datasets, SatIR retrieved 32–72% more relevant and eligible trials per patient compared to TrialGPT-style retrieval, suggesting meaningful potential gains for patients seeking trial access.
SatIR is a scalable information retrieval system developed to address the challenge of clinical trial matching, where finding an appropriate trial requires satisfying complex eligibility criteria—including negation, temporal conditions, numeric thresholds, and ontological relations—rather than simple semantic similarity. The system converts trial eligibility criteria and patient summaries into formal logical constraints, then executes those constraints over a structured database. It combines Satisfiability Modulo Theories (SMT) and relational algebra for rigorous, inspectable matching, while using large language models (LLMs) to handle ambiguous or incomplete clinical language and translate it into explicit constraint representations. Evaluated on the SIGIR 2016 patient–trial collection and the TREC-2022-RetrievalSubset benchmark, SatIR consistently outperformed similarity-based baselines, achieving 1.8–3.2× higher eligible-trial recall on the TREC dataset and retrieving 32–72% more relevant-and-eligible trials per patient on SIGIR 2016. Retrieval speed was also notable, with the system processing each patient query in approximately 146 milliseconds across 3,621 trials. The work is a preprint posted to arXiv and has not yet undergone formal peer review.
What's missing
As a preprint, SatIR has not undergone peer review. Key open questions include how the system performs on real-world, prospectively collected patient data outside benchmark datasets; how LLM-induced errors in constraint generation propagate to retrieval failures; and scalability to much larger trial registries (e.g., all of ClinicalTrials.gov).
What different sources said
- arXiv cs.AICenter
SatIR: Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching
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