AI in Pharmacovigilance: Systems, Agents & Governance · Section 13.2
~7 min read · The Drug Safety Coach — Global PV Career Course
Key points
Six AI types used in pharmacovigilance, and where each actually shows up
| AI Type | What It Is | Primary PV Application | 2026 Status |
|---|---|---|---|
| Rule-Based Automation | Predefined logic rules triggering actions on specific conditions — not machine learning, no training data | E2B(R3) field validation, timeline calculation, case routing | Fully mature since 2015-2018; foundational but not "AI" in the modern sense |
| Natural Language Processing (NLP) | Parses, interprets, and extracts meaning from human language text, structured or unstructured | ICSR intake from emails/PDFs, MedDRA code suggestion, literature screening, duplicate detection | Production deployment at most large pharma organisations as of 2025-26 |
| Machine Learning (ML) | Learns patterns from training data and applies them to new data without explicit per-case programming | Signal detection (disproportionality augmentation), case triage, semantic duplicate detection | Active production use; performance varies by training data quality |
| Generative AI (GenAI) / LLMs | Trained on vast text corpora, generates coherent, contextually appropriate text from structured inputs | Case narrative drafting, PBRER executive summary assistance, signal narrative drafting | Deployed in Vault Safety, NavaX, Oracle Argus (2025-26); all require physician review before submission |
| Deep Learning | Multi-layer neural networks learning hierarchical features from large datasets — a subset of ML | Combination product image analysis, complex EHR signal detection, ECG pattern recognition | Emerging, specialised; limited general PV production use as of 2026 |
| Agentic AI | Receives a goal and autonomously plans and executes a multi-step workflow using available tools, without human intervention between steps | End-to-end case processing, literature monitoring to ICSR creation, signal detection to preliminary report | Shipping in production — the frontier of 2026 PV AI deployment |
Full text
Before this module can talk meaningfully about what AI does in pharmacovigilance, it has to be precise about what "AI" actually means — because the term casually covers at least six genuinely distinct techniques, and treating them as one thing is exactly the kind of imprecision that causes real governance gaps.
At the simple end, rule-based automation applies predefined logic to trigger actions — checking whether a mandatory E2B(R3) field is populated, calculating a SUSAR reporting deadline from Day 0, routing a case based on product or reporter type. It requires no training data and isn’t machine learning at all; it’s been deployed in every major safety database platform since roughly 2015-2018, and it’s foundational to how those systems work even though nobody thinks of it as "AI" in the modern sense.
Natural language processing (NLP) is where most learners’ mental model of "AI in PV" actually starts: algorithms that parse and extract meaning from human language, structured or not. This is what reads an intake email or PDF and pulls out drug, event, and patient demographics; what suggests a MedDRA LLT from a verbatim description; what screens literature abstracts for relevance. Machine learning (ML) proper — algorithms that learn patterns from training data and apply them to new, unseen data — is what powers disproportionality-augmented signal detection, case triage, and semantic-similarity duplicate detection covered in Module 10.
Generative AI and large language models (GenAI/LLMs) are trained on vast text corpora and generate coherent, contextually appropriate text — this is what drafts case narratives (Module 5’s entire subject) and PBRER executive summaries (Module 8), and every deployment of it in a regulated PV workflow requires physician review before anything it produces gets submitted. Deep learning, a specialised subset of ML using multi-layer neural networks, shows up in narrower applications like combination-product image analysis or ECG pattern recognition in cardiac safety monitoring — genuinely emerging, with limited general PV deployment as of 2026. And agentic AI — covered in full in Lesson 13.5 — is qualitatively different from all the others: it receives a goal and autonomously plans and executes a multi-step workflow using whatever tools are available to it, without a human directing each individual step. This is the genuine frontier of 2026 PV AI deployment, and it’s shipping in production today, not roadmap-promised for later.
Note
A system that automatically checks whether a mandatory E2B(R3) field is populated is rule-based automation — it applies a fixed rule, not a learned pattern. A system that proposes a MedDRA code from a verbatim description by matching patterns it learned from millions of previously coded cases is machine learning. Both get casually called "AI" — but the governance requirements and the failure modes are genuinely different, and conflating them is a common, avoidable error.
Quick check
Test yourself before moving on — no pressure, just click an answer.
1. Why does it matter, beyond terminology precision, to distinguish rule-based automation from machine learning?
2. What makes agentic AI qualitatively different from NLP or ML tools used elsewhere in PV?