Drug Safety Agents Using Graphs and Ontologies
Journal:
medRxiv
Published Date:
Feb 5, 2026
Abstract
In pharmacovigilance, analyzing drug safety cases is often time consuming due to the abundance of laboratory data, complex medical histories, and intricate temporal relationships. Agentic AI systems can significantly reduce case processing time by assisting medical reviewers in surfacing clinically relevant evidence. However, previous studies have highlighted that large language models alone lack causal reasoning and evidence-based interpretability. To address these limitations, we present a knowledge-grounded safety case analysis framework that integrates disproportionality analysis to generate and prioritize potential adverse event hypotheses. Crucially, we introduce a novel hallucination-risk-aware execution planner that dynamically routes queries to the safest reasoning pathway, prioritizing deterministic graph retrieval over generative methods for clinically sensitive signals. The system demonstrates how structured medical knowledge and statistical evidence can be combined to support a reliable, explainable case assessment and can be readily extended with causal inference modules for deeper clinical reasoning.
