ReviewDrug safety2026
Artificial Intelligence in Pharmacovigilance: Guiding Principles from the CIOMS Working Group XIV.
Review in Drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is increasingly being explored to support pharmacovigilance activities including processes involving individual case safety reports. These technologies may improve efficiency and enable new analytical capabilities, but they also raise important questions regarding the responsible use of these systems in contexts that directly support regulatory decision-making and patient safety. In response to these opportunities and challenges, the Council for International Organizations of Medical Sciences (CIOMS) set up Working Group XIV on Artificial Intelligence in Pharmacovigilance to establish and promote principles and guidance for this area. The resulting CIOMS report was published in December 2025 and presents seven guiding principles: risk-based approach, human oversight, validity and robustness, transparency, data privacy, fairness and equity, and governance and accountability. To maximise stakeholder awareness and understanding, this article provides an overview of the CIOMS XIV guidance and illustrates how these principles may be applied in practice through selected use cases. The report aims to support stakeholders in developing and deploying AI systems in a manner that maintains scientific rigor, transparency, and public trust while enabling innovation to support safe use of medicines worldwide.
Identifiers
42645766What 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.