ArticleDrug safety2021
Artificial Intelligence, Real-World Automation and the Safety of Medicines.
Article in Drug safety, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical Safety Alerts.Drug safety · 2026Article
- Biomedical Large Language Models and Prompt Engineering for Causality Assessment of Individual Case Safety Reports in Pharmacovigilance.Pharmaceutical research · 2026Article
- Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.Therapeutic innovation & regulatory science · 2026Article
- Characteristics, preventability, management, and predictors of adverse drug reaction-related hospital admissions: a cross-sectional study in a Thai tertiary care hospital.Journal of pharmaceutical policy and practice · 2026Article
- 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
- Pharmacovigilance and bioethics: the importance of a neglected relationship.Frontiers in drug safety and regulation · 2025Review
- Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.Therapeutic advances in drug safety · 2025Review
- Appeal for Increasing the Impact of Pharmacovigilance.Drug safety · 2024Article
- Governance of artificial intelligence and machine learning in pharmacovigilance: what works today and what more is needed?Therapeutic advances in drug safety · 2024Article
- A discovery and verification approach to pharmacovigilance using electronic healthcare data.Frontiers in pharmacology · 2024Article
- Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities.Frontiers in drug safety and regulation · 2024Review
- Review
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- An industry perspective on the use of machine learning in drug and vaccine safety.Frontiers in drug safety and regulation · 2023Article
- Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review.Drug safety · 2022Article
- Supervised Machine Learning-Based Decision Support for Signal Validation Classification.Drug safety · 2022Article
- Article
- Artificial Intelligence and Machine Learning for Safe Medicines.Drug safety · 2022Article
- Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.Drug safety · 2022Article
- Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.Drug safety · 2022Article
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
Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.
Indexed as
Identifiers
33026641What 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.