AI in Pharmacovigilance

Overview of AI concepts relevant to life sciences

Key technologies: machine learning, natural language processing, automation

Relevance and strategic value of AI in pharmacovigilance

Techniques for AI-driven identification of adverse events from structured and unstructured data

Case studies on AI implementation in reporting systems

AI tools for detecting safety signals

Automated workflows for risk evaluation

Examples from industry implementation

Overview of current regulatory guidance and expectations

Ensuring AI applications meet compliance standards

Ethical and governance considerations in deploying AI

Emerging technologies and trends

Adapting teams and systems to future developments

Recap of key lessons

Open discussion and participant questions

Presentation Slides (Available for purchase)