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Intelligent Automation Improved Efficiency in Pharmacovigilance Safety Signal Assessment
Jeffrey Warner1, Luis Henrique De Souza Teodoro1, Anaclara Prada Jardim1
1Eli Lilly and Company, Indianapolis, Indiana, USA.
Clinical Pharmacology and Therapeutics
|July 29, 2026
Summary
Intelligent automation using GPT-4o can streamline pharmacovigilance by extracting data from individual case safety reports (ICSRs). This approach aids in detecting drug safety signals more efficiently while maintaining human oversight.
Area of Science:
- Pharmacovigilance and drug safety analysis.
- Application of artificial intelligence in healthcare.
- Natural Language Processing (NLP) for clinical data extraction.
Background:
- Pharmacovigilance identifies adverse events from drugs, biologics, or devices.
- Safety signals require comprehensive analysis of individual case safety reports (ICSRs).
- Manual ICSR narrative assessment is labor-intensive and prone to variability.
Purpose of the Study:
- To assess the utility of an intelligent automation system using GPT-4o for ICSR narrative analysis.
- To automate the extraction of key case elements for safety signal assessment.
- To evaluate the performance of AI in extracting risk factors, dechallenge, and rechallenge information.
Main Methods:
- A retrospective feasibility study using a proprietary platform with GPT-4o.
- Automated extraction of risk factors, dechallenge, and rechallenge data from ICSR narratives.
- Human expert oversight and verification of AI-extracted data.
Main Results:
- The AI system demonstrated performance ranging from F1=0.444 to 1.000 for risk factors.
- Performance for dechallenge and rechallenge responses ranged from F1=0.429 to 0.909.
- Potential time savings were identified, even with human verification of AI outputs.
Conclusions:
- Intelligent automation with GPT-4o shows potential for streamlining pharmacovigilance signal management.
- A machine-first, human-verified workflow can enhance efficiency while maintaining regulatory compliance.
- This study is the first to demonstrate such an AI-driven platform for signal management workflows.
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