Pharmacovigilance

Automate adverse event detection and reporting to enhance drug safety and compliance

Business impact

• case processing time — AI reduces manual review time, speeding up adverse event case handling
• operational efficiency — Automation lowers labor needs and streamlines pharmacovigilance workflows
• compliance adherence — AI ensures consistent regulatory reporting and audit readiness
• patient safety monitoring — Faster detection of adverse events improves patient risk management

Data requirements

• Unstructured clinical reports and patient narratives (Text) — Extract adverse event details using NLP from free-text documents
• Regulatory submission databases (Structured) — Provide structured data for case validation and reporting
• Medical literature and journals (Text) — Supplement safety signals and contextual evidence for adverse events
• Scanned documents and forms (Image) — Use OCR to digitize and process paper-based adverse event data

AI methods and techniques

• Predictive AI — Prioritize and classify adverse events for efficient case management
• Generative AI — Summarize case narratives and generate coding suggestions
• Agentic AI — Automate workflow orchestration with human-in-the-loop oversight

AI models and model families

Gemini, GPT-4o, Claude, Fine-tuned domain-specific LLMs

Industries

Real-world evidence

5 documented case studies on record.

Companies using this: Bayer, Elanco, Gleneagles Hospital Hong Kong, Graph AI, KPJ Healthcare.