Applying AI to Structured Real-World Data for Pharmacovigilance Purposes: Scoping Review
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) applied to structured real-world data (RWD) shows promise for pharmacovigilance. Further research is needed in explainable AI and data preprocessing protocols for drug safety.
Area Of Science
- Pharmacovigilance and Drug Safety
- Artificial Intelligence in Healthcare
- Real-World Data Analysis
Background
- Artificial intelligence (AI) applied to real-world data (RWD) is a promising paradigm for pharmacovigilance.
- Current research often focuses on unstructured RWD, necessitating an investigation into AI applications on structured RWD.
Purpose Of The Study
- To conduct a scoping review on the emerging use of AI on structured RWD for pharmacovigilance.
- To identify trends and potential research gaps in this field.
Main Methods
- Scoping review based on PRISMA methodology.
- Searched MEDLINE via PubMed for studies from January 2010 to January 2024.
- Evaluated studies based on AI approaches, data preprocessing, clinical validation, and trustworthy AI criteria.
Main Results
- 36 studies were included, with a significant increase post-2019.
- Most studies focused on adverse drug reaction detection using nonsymbolic AI, particularly random forest.
- Electronic health records were the primary RWD source, often lacking interoperability; code availability and clinical validation were limited.
Conclusions
- AI with structured RWD is promising for drug safety but requires further exploration of explainable and causal AI.
- Standardized RWD preprocessing protocols are needed.
- Evaluation procedures for sensitive data require further investigation.
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