Smarter Drug Safety Automation: Strategies for Leveraging Generative AI in Pharmacovigilance
Explore how life sciences teams are using generative AI to streamline drug safety case intake—without compromising quality, compliance or patient safety.
In the fast-paced world of biopharmaceutical clinical development, speed and precision aren’t just ambitions—they’re imperatives. Whether you're overseeing protocol design, managing safety case intake or preparing regulatory documents, operational efficiency can make or break your timeline.
Explore how our experts can help transform your pharmacovigilance operations.
Enter generative AI (genAI). GenAI is helping contract research organizations (CROs) and life sciences teams automate the high-volume, repetitive tasks that often slow down pharmacovigilance (PV) operations, without sacrificing data integrity or compliance. From transforming unstructured safety data into structured fields to accelerating narrative generation, custom large language models (LLMs) are reshaping how teams manage their clinical and regulatory workloads.
So, how can PV leaders unlock real value from AI in drug safety? Our experts outline four strategies for implementing AI-enabled case intake and PV automation while prioritizing patient safety, audit readiness and human oversight.
Lesson 1: Treat Human-in-the-Loop as a Strategic Feature
For any AI-enabled PV system, human-in-the-loop (HITL) is not just a safeguard, it’s a design principle. While generative AI can extract data from emails, scanned documents and Individualized Case Safety Reports (ICSRs), it lacks the contextual judgment needed to assess case seriousness or causality. That’s where experienced reviewers come in.
By pairing AI with domain expertise, organizations can reduce manual workload while improving overall data quality. Over time, this human feedback becomes fuel for continuous AI improvement, allowing the system to get smarter while maintaining compliance and trust.
Lesson 2: Prioritize Use Cases Where AI Delivers Real Efficiency
Not every process benefits equally from automation. In pharmacovigilance, the most effective AI applications start with high-friction, high-volume tasks such as classifying email intake, parsing lab reports or generating patient narratives.
Custom Chat Generative Pre-Trained Transformers (GPTs) can:
- Automatically classify incoming emails and attachments
- Pre-fill structured safety fields
- Flag missing or non-compliant information
- Generate first drafts of narratives for faster review
These use cases can offer fast return on investment (ROI) and pave the way for expanding automation to more complex areas like Medical Dictionary for Regulatory Activities (MedDRA) coding or signal detection.
Lesson 3: Design for Flexibility, Not Forms
One common failure point in PV automation? Systems that rely on custom forms or templates for every sponsor or document type. These rigid structures don’t scale.
Instead, successful AI tools are trained to generalize. By using natural language processing (NLP) and domain-specific fine tuning, generative models can extract safety data from a wide range of unstructured sources without the need for manual setup.
This flexibility enables AI to keep up with real-world complexity and variation across sponsors, geographies and therapeutic areas.
Lesson 4: Balance Innovation with Regulatory Readiness
Generative AI can increase efficiency but it must operate within a tightly regulated ecosystem. From E2B(R3) compatibility to audit trails, any system supporting safety data management must be defensible during inspections.
That means:
- Clear delineation of AI-generated vs. human-reviewed fields
- Full traceability of source documents and AI decisions
- Documentation of prompt tuning and model versioning
- XML and PDF outputs that meet agency expectations
Organizations that build AI systems with compliance in mind will be better positioned to scale automation responsibly.
Final Thought: Empower PV Experts, Don’t Replace Them
Generative AI is not here to take over drug safety—it’s here to limit the burdens that get in the way of good science. By automating the administrative lift, AI gives PV professionals more time to focus on strategic decision-making, patient risk assessments and regulatory strategy.
Whether you're modernizing your safety case intake or building broader automation into your clinical operations, the smartest approach isn’t to go faster—it’s to go forward with confidence, clarity and control.
Contributors
Jonathon Romero, PhD | Principal Safety & PV Operations Specialist
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