Automating postmarketing pharmacovigilance with large language model agents

MedCheckpoint, a multi-agent LLM system for postmarketing pharmacovigilance, achieved higher report quality (overall means 4.81–4.88 vs 4.25–4.55 and 4.02–4.29) on a 2.7 million–person simulated EHR benchmark.

• Why it matters: Postmarketing pharmacovigilance is hindered by underreporting and delayed risk detection, and generalist LLM agents can make ad hoc analytical choices that reduce epidemiological validity and reproducibility.
• What they did: The system grounds each analytical step in a curated, source-traceable knowledge base, locks study-design parameters before execution, runs patient-level analyses using human-written deterministic statistics, and applies checkpoint rules to abort infeasible analyses, with optional human review.
• The result: Across a 54-question benchmark, MedCheckpoint also showed better run-to-run consistency (within-question standard deviation 0.116–0.186 vs up to 0.440) and avoided misleading failures on seven stress-test trap questions with known data limitations.

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