Evidence map›Paper›PMID 41431473›Full record

ReviewTherapeutic advances in drug safety2025

Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.

Darmendra Ramcharran, Jeffery L Painter, Vijay Kara, Michael Glaser, Marco Vanini, Venkateswara Rao Chalamalasetti, Christopher Golds, Azza Abdelkarim, Andrew Bate, Jens-Ulrich Stegmann

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Therapeutic advances in drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. How is AI developing in pharmacovigilance?Therapeutic advances in drug safety · 2026
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Darmendra RamcharranGSK, 1000 Winter Street, Waltham, MA 02451, USA University of Rhode Island, Kingston, RI, USA.ORCID https://orcid.org/0009-0009-2787-5265
Jeffery L PainterGSK, Durham, NC, USA.ORCID https://orcid.org/0000-0001-9651-9904
Vijay KaraGSK, London, UK.
Michael GlaserGSK, Upper Providence, PA, USA.
Marco VaniniGSK, Wavre, Belgium.
Venkateswara Rao ChalamalasettiGSK, Durham, NC, USA.
Christopher GoldsGSK, London, UK.
Azza AbdelkarimGSK, London, UK.
Andrew BateGSK, London, UK.
Jens-Ulrich StegmannGSK, Wavre, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The advent of generative artificial intelligence (GenAI) has introduced both remarkable opportunities and significant challenges in the field of pharmacovigilance (PV). This perspective review reflects on emerging trends, practical use cases, and conceptual frameworks shaping the integration of GenAI in high-risk domains such as drug and vaccine safety monitoring. We draw on current experiments and early real-world applications to examine the potential benefits, inherent risks, and propose a framework for integrating GenAI into PV systems, emphasizing the necessity of rigorous testing, human oversight, and ethical considerations. Our goal is to support PV professionals and stakeholders in navigating this rapidly evolving landscape by identifying promising strategies and implementation pathways.

Indexed as

drug safetygenerative AIpharmacovigilance

Identifiers

PMID41431473
PMCPMC12718344

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.