Evidence map›Paper›PMID 39331228›Full record

ArticleDrug safety2025

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.

Ashish Jain, Maribel Salas, Omar Aimer, Zahabia Adenwala

Abstract read
PubMed Publisher
In one paragraph

Article in Drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

4 authors.

Ashish JainCuris Inc., 128 Spring Street, Suite 500, Lexington, MA, 02421, USA. JainashishL89@gmail.com.ORCID 0009-0008-2384-3111
Maribel SalasBayer Pharmaceuticals, Whippany, NJ, USA.ORCID 0000-0001-5634-1558
Omar AimerInnovigilance, Montreal, QC, Canada.ORCID 0000-0002-0874-486X
Zahabia AdenwalaSeres Therapeutics, Cambridge, MA, USA.ORCID 0009-0007-4192-6116

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

Indexed as

Artificial IntelligencePharmacovigilanceAdverse Drug Reaction Reporting SystemsDrug-Related Side Effects and Adverse ReactionsHumansPatient SafetyRisk Assessment

Identifiers

What OpenQuestion holds

Textmetadata
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.