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Related Experiment Videos

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

Tarek A Hammad1, Justine Rochon2, Salman Afsar3

  • 1Patient Safety and Pharmacovigilance (PSPV), Takeda Development Center Americas, Inc., Cambridge, USA. Tarek.hammad@takeda.com.

Drug Safety
|July 15, 2026
PubMed
Summary

Related Concept Videos

Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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9<sup>th</sup> ISoP Intelligent Automation Boston Seminar: From Innovation to Impact. Building Trustworthy AI in Pharmacovigilance 4-5 December 2025 | Cambridge, USA & Virtual.

Drug safety·2026

This study introduces PV-SCOPE, an AI-enabled pharmacovigilance system to improve drug safety signal assessment. It aims for a more efficient, harmonized, and scientifically rigorous approach to confirm potential adverse event causality.

Area of Science:

  • Pharmacovigilance and Drug Safety
  • Artificial Intelligence in Healthcare
  • Regulatory Science

Background:

  • Current safety signal assessment relies on manual methods, leading to inefficiencies and variability.
  • Existing pharmacovigilance approaches struggle to integrate diverse evidence streams effectively.
  • There's a need for enhanced efficiency and harmonization in confirming potential drug-related adverse events.

Purpose of the Study:

  • To propose an AI-enabled framework, PV-SCOPE, for integrated safety signal causality assessment.
  • To outline design considerations for an efficient and harmonized approach to signal confirmation.
  • To demonstrate how a holistic causality framework can be operationalized using multimodal evidence.

Main Methods:

  • Conceptual framework design for an AI-enabled approach (PV-SCOPE).

Related Experiment Videos

  • Integration of multimodal evidence streams for causality assessment.
  • Operationalization of the Hammad-Afsar holistic causality assessment framework using AI.

    • Main Results:

    • The article presents the architectural logic, governance, and validation requirements for PV-SCOPE.
    • Focus is on the design and conceptualization, not empirical performance results.
    • The proposed framework emphasizes preserving clinical and scientific reasoning within the AI workflow.

    Conclusions:

    • PV-SCOPE offers a design blueprint for a more efficient and harmonized AI-enabled safety signal assessment process.
    • The approach aims to mitigate limitations of current manual methods in pharmacovigilance.
    • Successful implementation requires careful consideration of governance, validation, and regulatory aspects.