ReviewInternational journal of clinical pharmacy2025
Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.
Review in International journal of clinical pharmacy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- Evaluation of large language models in supporting autoimmune liver disease diagnosis and clinical decision-making: advantages of reasoning-based models.International journal of clinical pharmacy · 2026Article
- Development and validation of a machine learning-based clinical decision support tool for stratifying intravenous medication risk in hospitalized patients with heart failure.International journal of clinical pharmacy · 2026Article
- Intelligent Automation Improved Efficiency in Pharmacovigilance Safety Signal Assessment.Clinical pharmacology and therapeutics · 2026Article
- Toward more accurate adverse event attribution in multiple myeloma clinical trials.Blood cancer journal · 2026Review
- A parallel dual-stream state-space module for reliable and efficient biomedical relation extraction.PLoS computational biology · 2026Article
- Early prediction and risk assessment of adverse drug combinations using ensemble learning.Scientific reports · 2026Article
- Multi-Output Probabilistic Prediction of Drug Side Effects Using Classical Machine Learning Algorithms.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Carcinogenic Medications: A Review of Specific Agents and Molecular Mechanisms of Carcinogenesis.Cancer reports (Hoboken, N.J.) · 2026Review
- Article
- Post-marketing safety of tarlatamab in small cell lung cancer based on FAERS and WHO-VigiAccess with SHAP-based interpretable machine learning analysis of immune-related adverse events.Frontiers in pharmacology · 2026Article
- Natural products and nutraceuticals in the management of chronic inflammatory diseases: efficacy, mechanisms, and comparative insights.Inflammopharmacology · 2025Review
- Evaluating the effectiveness of AI-enhanced "One Body, Two Wings" pharmacovigilance models in China: a nationwide survey on medication safety and risk management.Frontiers in health services · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPharmacovigilance is vital for monitoring adverse drug reactions (ADRs) and ensuring drug safety. Traditional methods are slow and inconsistent, but artificial intelligence (AI), through automation and advanced analytics, improves efficiency and accuracy in managing increasing data complexity.
aimTo explore AI's practical applications in pharmacovigilance, focusing on efficiency, process acceleration, and task automation. It also examines the use of an expert-defined Bayesian network for causality assessment in a Pharmacovigilance Centre, demonstrating its impact on decision-making.
methodA comprehensive literature narrative review was conducted in MEDLINE (via PubMed), Scopus, and Web of Science using a set of targeted keywords, including but not limited to "pharmacovigilance", "artificial intelligence", "adverse drug reactions" and "drug safety". Relevant studies were analysed without restrictions on publication year or language. The search was carried out in January 2025.
resultsAI has greatly improved pharmacovigilance by streamlining signal detection, surveillance, and ADR reporting automation. Techniques like data mining and automated signal detection have expedited safety signal identification, while duplicate detection has enhanced data precision in safety evaluations. AI has also refined real-world evidence analysis, deepening drug safety and efficacy insights. Predictive models now anticipate ADRs and drug-drug interactions, enabling proactive patient care. At a regional pharmacovigilance center, the implementation of an expert-defined Bayesian network has optimized causality assessment, reducing processing times from days to hours, minimizing subjectivity, and improving the reliability of drug safety evaluations.
conclusionAI holds significant promise for enhancing pharmacovigilance practices, yet its practical application remains primarily confined to academic research, with integration hindered by data quality issues, regulatory barriers, and the need for more transparent algorithms.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.