Xtalks — The Life Science Community™, Online webinar
2026-10-28
Pharmacovigilance intake processes must keep pace with the growing volume and variety of incoming data. Potential adverse events and product quality complaints can arrive through emails, lab reports, patient portals, helpline calls, physician notes, social media and many other channels. While automation is becoming part of the intake process, most pharmacovigilance teams still rely on a combination of automated workflows and manual review to assess, classify and route each case. This webinar explores how AI-enabled adverse event detection and intake can help teams manage this increasing volume while maintaining appropriate human oversight. As data volumes grow, hybrid processes can create delays, increase workload and introduce inconsistency, making it difficult to scale efficiently without compromising quality. The opportunity is to automate case intake where appropriate, while preserving the human oversight needed for complex decisions. The result is a more efficient, consistent, scalable and quality-focused pharmacovigilance process. The session will examine practical considerations for introducing AI into a regulated pharmacovigilance environment, including embedded quality checks, transparent confidence indicators, auditability and end-to-end traceability of extracted information and resulting decisions. The goal is not to replace human oversight but to automate appropriate activities and focus expert attention where it adds the most value. A live demonstration of ElevateSafety™ and Detective will illustrate how AI-enabled detection, data structuring, quality checks and routing can support adverse event intake across diverse data sources. Whether an organization is evaluating AI for the first time or benchmarking an existing approach, this session will provide a practical, grounded view of what's possible today for pharmaceutical and life sciences safety operations. Register for this webinar to learn how pharmacovigilance teams can use AI to manage rising data volumes without sacrificing quality, compliance or human accountability.
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