Evidence map›Paper›PMID 39360710›Full record

ReviewEuropean journal of pain (London, England)2025

Navigating the light and shadow of scientific publishing faced with machine learning and generative AI.

Federico Palmisani, Daniel Segelcke, Jan Vollert

Abstract readReview
In one paragraph

Review in European journal of pain (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Federico PalmisaniDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
Daniel SegelckeDepartment of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital, Muenster, Germany.
Jan VollertDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.ORCID https://orcid.org/0000-0003-0733-5201

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe public release of ChatGPT in November 2022 sparked a boom and public interest in generative artificial intelligence (AI) that has led to journals and journal families hastily releasing generative AI policies, ranging from asking authors for acknowledgement or declaration to the outright banning of use.

resultsHere, we briefly discuss the basics of machine learning, generative AI, and how it will affect scientific publishing. We focus especially on potential risks and benefits to the scientific community as a whole and journals specifically.

conclusionWhile the concerns of editors, for example about manufactured studies, are valid, some recently implemented or suggested policies will not be sustainable in the long run. The quality of generated text and code is quickly becoming so high that it will not only be impossible to detect the use of generative AI but would also mean taking a powerful tool away from researchers that can make their life easier every day. SIGNIFICANCE: We discuss the history and current state of AI and highlight its relevance for medical publishing and pain research. We provide guidance on how to act now to increase good scientific practice in the world of ChatGPT and call for a task force focusing on improving publishing pain research with use of generative AI.

Indexed as

Artificial IntelligenceMachine LearningPublishingHumans

Identifiers

PMID39360710
PMCPMC11755395

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.