The results indicate that follow-up improves understanding when it resolves clinical uncertainty, but is inefficient when pursued without regard to impact, supporting a shift to proportionate, purpose-driven follow-up strategies for consistent and efficient use of resources.
V. Kara, F. van Hunsel, Manal Younus et al.· Drug Safety· 0 citations
This study highlights the potential of ML-based tools to improve pharmacovigilance by enhancing signal detection performance, reducing the likelihood of missed signals, while increasing operational efficiency, and strengthening reproducibility and transparency.
Luciano Ciccarelli, Olivia Mahaux, Christie Roshan et al.· Drug Safety· 0 citations
Predictive safety is used primarily to mean product-level and population- or subgroup-level anticipation of plausible treatment-related harm, rather than an autonomous patient-level clinical decision-making approach.
Tarek A. Hammad, Justine Rochon, Kate Gofman et al.· Drug Safety· 0 citations
An overview of the CIOMS XIV guidance on Artificial Intelligence in Pharmacovigilance presents seven guiding principles and illustrates how these principles may be applied in practice through selected use cases.
G. N. Norén, Julie Durand, V. Kara et al.· Drug Safety· 0 citations
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