ArticleDrug safety2022
"Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?
Article in Drug safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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
40 citing papers in PubMed.
- A Scoping Review of Case-Level Causality Assessment Tools Developed Between 2008 and 2023; Strengths, Weaknesses and Potential Future Improvements.Drug safety · 2026Article
- Intelligent Automation Improved Efficiency in Pharmacovigilance Safety Signal Assessment.Clinical pharmacology and therapeutics · 2026Article
- Methodological and regulatory considerations for causal AI in drug development.NPJ digital medicine · 2026Review
- Agglomerative clustering-based drugs graded for the risk signals of drug-induced cognitive disorder: a study on the FAERS database.Therapeutic advances in drug safety · 2026Article
- Does generative AI mean the "end of history" for pharmacovigilance automation? towards a framework for the future of human-AI systems.Frontiers in drug safety and regulation · 2026Article
- A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development.Journal of medicinal chemistry · 2025Article
- FDA Adverse Event Reporting System (FAERS) Essentials: A Guide to Understanding, Applying, and Interpreting Adverse Event Data Reported to FAERS.Clinical pharmacology and therapeutics · 2025Article
- Future Designs of Clinical Trials in Nephrology: Integrating Methodological Innovation and Computational Power.Sensors (Basel, Switzerland) · 2025Review
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.International journal of clinical pharmacy · 2025Review
- Disproportionality analysis and risk factor assessment of drug-associated thyroid dysfunction adverse events: a study based on the FAERS database.Endocrine connections · 2025Article
- Artificial Intelligence: Applications in Pharmacovigilance Signal Management.Pharmaceutical medicine · 2025Review
- The Beginning of a "Regulatory Renaissance": Positioning Regulatory Coverage at the Interface of Human Expertise and Digital Support.Therapeutic innovation & regulatory science · 2025Article
- Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.Drug safety · 2025Article
- How LLMs can advance safety case intake-points to consider and insights from a proof of concept.Therapeutic advances in drug safety · 2025Review
- The applications and advances of artificial intelligence in drug regulation: A global perspective.Acta pharmaceutica Sinica. B · 2025Review
- Pharmacovigilance - Technological Advancements, Recent Developments and Innovations.Current drug safety · 2025Review
- Article
- Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971-2024).Medical journal of the Islamic Republic of Iran · 2025Article
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine.Pharmaceutics · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There is great interest in the application of 'artificial intelligence' (AI) to pharmacovigilance (PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application of AI to the processing and evaluation of Individual Case Safety Reports (ICSRs) submitted to the FDA Adverse Event Reporting System (FAERS). We describe a general framework for considering the readiness of AI for PV, followed by some examples of the application of AI to ICSR processing and evaluation in industry and FDA. We conclude that AI can usefully be applied to some aspects of ICSR processing and evaluation, but the performance of current AI algorithms requires a 'human-in-the-loop' to ensure good quality. We identify outstanding scientific and policy issues to be addressed before the full potential of AI can be exploited for ICSR processing and evaluation, including approaches to quality assurance of 'human-in-the-loop' AI systems, large-scale, publicly available training datasets, a well-defined and computable 'cognitive framework', a formal sociotechnical framework for applying AI to PV, and development of best practices for applying AI to PV. Practical experience with stepwise implementation of AI for ICSR processing and evaluation will likely provide important lessons that will inform the necessary policy and regulatory framework to facilitate widespread adoption and provide a foundation for further development of AI approaches to other aspects of PV.
Indexed as
Identifiers
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.