Commentary highlights the potential of explainable AI to improve signal detection in pharmacovigilance, suggesting a shift towards causally informed models.
Key Points
Explainable AI can improve signal detection in pharmacovigilance, enhancing transparency and reliability.
Causal AI methods face challenges but promise more interpretable outputs compared to traditional machine learning models.
The integration of causal inference in AI workflows is essential for scientifically credible pharmacovigilance applications.
Developing benchmark datasets is crucial for evaluating AI models against clinical and regulatory standards.
Cite This Study
Ferreira‐da‐Silva et al. (2025) studied this question.