Detecting Contradictory Evidence on Drug-Disease Associations at Scale

Contradictory claims about drug efficacy across randomized controlled trials (RCTs) complicate evidence-based medicine, yet existing automated methods classify isolated sentence pairs and do not explain why studies disagree. We present DrugCon, a large language model (LLM)-based framework that extracts study conclusions from RCT abstracts, links these claims when they concern the same drug-disease association, detects contradictions with counterfactual reasoning, and explains them using a taxonomy of ten study-level factors such as population, dosage, and outcome measures. Applying DrugCon to

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