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AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review
Miki Ohta1, Miki Ota2, Mikihiko Ohta3
1Clinical Research Promotion Center, The University of Tokyo Hospital, Tokyo, Japan.
JMIR Research Protocols
|July 16, 2026
Summary
This scoping review examines artificial intelligence (AI) methods for causality assessment in pharmacovigilance. It maps AI applications, data needs, and risks to improve patient safety and drug development.
Area of Science:
- Pharmacovigilance and Drug Safety
- Artificial Intelligence in Healthcare
- Causality Assessment Methodologies
Background:
- Pharmacovigilance is crucial for patient safety, focusing on identifying and managing adverse drug events.
- Causality assessment of adverse events is vital but increasingly complex due to data volume and intricacy.
- Existing research on AI applications in pharmacovigilance causality assessment, including data requirements and risks, is limited.
Purpose of the Study:
- To systematically review and map the evidence on artificial intelligence (AI)-based methods for causality assessment in pharmacovigilance.
- To characterize AI applications, focusing on functional roles, data inputs, information needs, and associated risks.
- To compare AI use at individual case and population levels, identify AI techniques, and summarize data quality and governance.
Main Methods:
- A comprehensive search of multiple databases (PubMed, Web of Science, etc.) for AI-based approaches in causality assessment.
- Inclusion of data-driven models (machine learning, NLP, knowledge graphs, causal inference) and knowledge-based systems.
- Systematic data charting and reflexive thematic analysis, adhering to PRISMA-ScR and PRISMA-S guidelines.
Main Results:
- Protocol registered on Open Science Framework (OSF) with updates regarding data collection periods.
- Preliminary screening identified 196 articles from 760 records for full-text review.
- Database searches, data charting, and synthesis are scheduled, with findings expected by late 2026.
Conclusions:
- The review will provide a comprehensive overview of AI applications in pharmacovigilance causality assessment.
- Findings will clarify data requirements, quality considerations, and risk management strategies for AI implementation.
- Expected to guide methodological advancements, practical applications, and governance of AI in drug safety.
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