Augmenting Medical Judgment in Signal Confirmation: Design Considerations for an AI-Enabled Causality Assessment Framework.

Abstract

The safety signal assessment process evaluates the potential causal association between a medicinal product and a specific adverse event by integrating multiple streams of evidence. Existing causality assessment methods remain challenged by the considerable manual effort involved and by decision variability, even when drawing on the same underlying domains of evidence. This work addresses the need in contemporary pharmacovigilance for a more efficient and harmonized approach to signal confirmation that preserves scientific rigor while mitigating the limitations of the current assessment processes. To address this need, the article describes design considerations for an artificial intelligence (AI)-enabled, integrated approach for safety signal causality assessment, referred to as Signal Confirmation through Omnichannel Pharmacovigilance Evidence (PV-SCOPE). As a conceptual proof-of-principle, the proposed approach describes how the Hammad-Afsar holistic causality assessment framework could be operationalized through multimodal evidence integration and structured approach, while preserving the central role of clinical and scientific reasoning in causality assessment. Because the AI workflow is presented as a design-oriented framework rather than a completed or validated model, the article focuses on architectural logic, governance, and validation and regulatory requirements rather than reporting performance results.