# AI-Powered Signal Detection
Signal detection is the process of identifying previously unknown or incompletely documented adverse drug reactions. Traditional approaches rely primarily on statistical disproportionality analysis of spontaneous reporting databases — calculating whether a drug-event combination is reported more frequently than expected.
Limitations of Traditional Methods
Disproportionality methods (PRR, ROR, EBGM, IC) are valuable but have significant limitations: - They rely on spontaneous reporting, which captures only 1-10% of actual adverse events - They are retrospective — by the time a signal is statistically detectable, many patients may have been affected - They analyze one data source in isolation, missing contextual information - They generate large numbers of statistical signals that require manual triage
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What you'll learn:
- Understand disproportionality analysis and how AI enhances traditional signal detection methods
- Apply AI to identify emerging safety signals from multiple data sources simultaneously
- Use AI to generate signal evaluation summaries that integrate clinical and statistical evidence