Neurosymbolic Clinical Trial Matching via LLM-Driven Abduction and Logical Verification

Researchers introduce αNeSy-CTM, a hybrid neurosymbolic framework combining Large Language Models with logical verification to automate clinical trial matching. The system achieves 30% relative improvement over zero-shot baselines by leveraging LLM language capabilities alongside formal symbolic reasoning to handle incomplete patient records and complex eligibility criteria.

Clinical trial matching represents a critical bottleneck in healthcare research, requiring precise alignment between patient characteristics and trial eligibility criteria. Traditional LLM approaches excel at natural language understanding but struggle with the deterministic logic required to verify complex medical eligibility rules, while purely symbolic systems demand complete, structured data that real clinical environments rarely provide. The αNeSy-CTM framework addresses this fundamental limitation by integrating abductive reasoning—a form of inference that generates plausible explanations from incomplete evidence—with symbolic logical verification.

This research reflects growing recognition within AI that hybrid approaches outperform monolithic solutions for domain-specific problems. The 30% performance improvement over baseline LLM systems substantiates this thesis, particularly for healthcare applications where accuracy directly impacts patient outcomes and research quality. The framework's demonstrated improvement in specificity and robustness indicates it reduces false positives, which carries substantial clinical significance.

For the healthcare technology sector, this methodology extends beyond trial matching into broader medical AI applications requiring both linguistic understanding and formal verification. Healthcare providers and clinical research organizations face persistent challenges automating eligibility assessment; this work offers a scalable, auditable alternative to manual review processes. The complementarity with Chain-of-Thought reasoning suggests multiple reasoning pathways enhance system reliability.

Developing production implementations requires addressing real-world data quality variations and integration with existing electronic health record systems. The research establishes technical feasibility but clinical deployment demands validation on diverse patient populations and integration testing with operational trial infrastructure. Future work should examine generalization across different trial types and disease domains.

• →Hybrid neurosymbolic approach achieves 30% relative improvement over standalone LLM baselines in clinical trial matching
• →Abductive reasoning enables the system to handle incomplete patient records and noisy clinical evidence more effectively than non-abductive methods
• →Framework combines LLM linguistic capabilities with formal logical verification to ensure deterministic accuracy for complex eligibility criteria
• →Results demonstrate improved accuracy, specificity, and robustness, reducing false positives critical for clinical applications
• →Complementarity with Chain-of-Thought reasoning suggests hybrid routing policies could further enhance performance in production systems