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Pharmacovigilance and safety
Reading every adverse event, not the ones with time to spare
Pharmacovigilance teams still intake adverse events, code them to MedDRA, and read free text narratives for safety signals largely by hand. A signal buried in an unstructured report can take as long to find as the report took to write.
AI reads at the volume the mandate requires. Document AI extracts adverse events from intake forms and free text narratives. Predictive models code to MedDRA with a confidence score attached, and flag the signal a narrative is carrying before a person searches for it. Generative AI drafts the regulatory submission a reviewer signs off on. A medical reviewer still signs every case, running on infrastructure you control.
4 use cases
Biomedical literature and knowledge graph synthesisAI biomedical literature synthesis reads publications and internal reports, maps genes and targets, and answers questions with a citation for each statement.Classifying patient-reported drug reviewsAI reads what patients write about a drug and sorts each review as effective, ineffective or adverse, routing every possible adverse mention to a person.Drafting safety reports and submission sectionsAI drafts cited sections of safety reports and regulatory submissions from approved sources, and checks a package for gaps, for a qualified person to sign.Pharmacovigilance case intake and signal detectionAgents turn adverse event reports into structured cases, suggest coding terms for your coders to confirm, and surface possible safety signals in narratives.
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