Fraud in Medical Publications.


Journal

Anesthesiology clinics
ISSN: 1932-2275
Titre abrégé: Anesthesiol Clin
Pays: United States
ID NLM: 101273663

Informations de publication

Date de publication:
Dec 2024
Historique:
medline: 24 10 2024
pubmed: 24 10 2024
entrez: 23 10 2024
Statut: ppublish

Résumé

This review highlights the increasing prevalence of fraudulent data and publications in medical research, emphasizing the potential harm to patients and the erosion of trust in the medical community. It discusses the impact of low-quality studies on clinical guidelines and patient safety, emphasizing the need for prompt identification. The review proposes using machine learning and artificial intelligence as potential tools to detect anomalies, plagiarism, and data manipulation, potentially improving the peer review process. Despite the acknowledgment of this problem and the growing number of retractions, the review notes a lack of focus on the clinical implications of forged evidence.

Identifiants

pubmed: 39443033
pii: S1932-2275(24)00012-0
doi: 10.1016/j.anclin.2024.02.004
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

607-616

Informations de copyright

Copyright © 2024 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure C. Gianluca Nato has nothing to disclose. F. Bilotta has nothing to disclose.

Auteurs

Consolato Gianluca Nato (CG)

Department of Anesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I, "Sapienza" University of Rome, Rome 00185, Italy.

Federico Bilotta (F)

Department of Anesthesiology, Critical Care and Pain Medicine, Policlinico Umberto I, "Sapienza" University of Rome, Rome 00185, Italy. Electronic address: federico.bilotta@uniroma1.it.

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Classifications MeSH