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The Potential of Automation and AI/ML in Causality Assessment for Safety Vigilance Causality assessment, which determines the relationship between a drug and an adverse event, is critical in safety vigilance. It helps identify new signals, measure the strength of evidence, and evaluate the benefit-risk profile of pharmaceutical medicinal products. This process has traditionally been performed manually by experts, but the emergence AI/ML technologies present an opportunity to automate it. This article will explore the various AI/ML models and methods that can be used to implement automated causality assessment in safety vigilance, along with the challenges and opportunities associated with this approach.
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ausality assessment is a crucial process in safety vigilance that involves determining the relationship between a drug and an adverse event or reaction. The identification of new signals,
Ryanka Chauhan Product Manager Datafoundry
evaluation of benefit-risk profile, and measurement of evidence strength for pharmaceutical medicinal products heavily rely on the factor of causality. Traditionally, this process has been performed manually w w w. p h a r m a f o c u s e u r o p e . c o m
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