Pharmacovigilance AI

Context

Pharmacovigilance systems operate in a high-stakes setting where precision, traceability, and workflow design matter as much as modeling performance. The aim is to reduce review burden while preserving clinical and operational confidence.

Focus areas

• AI support for adverse-event detection and structured understanding.
• Workflows that connect modeling outputs to safety-review needs.
• System designs that support analyst review instead of replacing expert judgment.

System Considerations

• Extraction quality must remain interpretable enough for regulated review processes.
• Models need to surface uncertainty and edge cases clearly.
• Success depends on fitting AI into real expert workflows.

Why It Matters

Healthcare AI is most useful when it improves signal detection and decision support without making oversight harder. This work sits at the intersection of NLP, workflow design, and practical reliability.