AI USE CASE
Automated Adverse Drug Event Detection
Automatically scan medical literature and clinical reports to surface adverse drug events for pharmacovigilance teams.
What it is
By prioritising signals automatically, pharmacovigilance teams can triage and investigate safety signals faster, cutting median signal detection lag from weeks to days. This lowers regulatory non-compliance risk and supports timely submissions to health authorities such as the EMA or FDA.
Data you need
Structured and unstructured clinical safety data including case reports, medical literature feeds (PubMed, EMBASE), spontaneous adverse event databases (e.g. EudraVigilance, FAERS), and internal pharmacovigilance records.
Required systems
- data warehouse
Why it works
- Involve regulatory affairs and medical reviewers during model design to align on acceptable precision/recall thresholds.
- Establish a continuous retraining pipeline with curated, expert-labelled adverse event annotations.
- Integrate with existing pharmacovigilance case management systems to embed AI-assisted triage into existing workflows.
- Maintain a full audit trail and explainability layer to satisfy EMA and ICH E2E guideline requirements.
How this goes wrong
- High false-positive rate overwhelms pharmacovigilance reviewers and erodes trust in the system.
- Insufficient training data for rare adverse events leads to poor recall on critical safety signals.
- Model is not retrained as medical terminology and drug portfolios evolve, causing performance drift over time.
- Regulatory expectations for auditability and explainability are not met, blocking submission use.
When NOT to do this
Do not deploy this as a fully autonomous signal suppression tool without mandatory human expert review at each decision point, regulatory frameworks require documented human oversight for all pharmacovigilance safety signals.
Example vendors
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