Evidence map›Paper›PMID 40881186›Full record

ArticleBig data and cognitive computing2024

Almost Nobody Is Using ChatGPT to Write Academic Science Papers (Yet).

Heather Desaire, Madeline Isom, David Hua

Abstract read
In one paragraph

Article in Big data and cognitive computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Heather DesaireDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.ORCID 0000-0002-2181-0112
Madeline IsomDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.ORCID 0000-0001-8533-9246
David HuaDepartment of Chemistry, University of Kansas, Lawrence, KS 66045, USA.

Funding

Mass Spectrometry Measurements for Complex PTMsR35GM130354 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI DESAIRE, HEATHER R · 2019 to 2023
$1.9M
NIGMS NIH HHS R35 GM130354
6 · The paper itself

Abstract

We assessed 19,000 scientific introductions to measure the level of undisclosed use of ChatGPT in scientific papers published in 2023 and early 2024. We applied a "stylistics" approach that has previously been shown to be effective at differentiating AI-generated text from human-written text in a variety of venues. Ten different MDPI journals were selected for this study, and the rate of use of undisclosed AI writing in these journals was fairly consistent across the journals. We estimate that ChatGPT was used for writing or significant editing in about 1 to 3% of the introductions tested. This analysis is the first systematic study of detecting undisclosed ChatGPT in published manuscripts in cases where obvious indicators, such as phrases like "regenerate response", are not present. The work demonstrates that generative AI is not polluting mainstream journals to any appreciable extent and that the overwhelming majority of scientists remain hesitant to embrace this tool for late-stage writing and editing.

Indexed as

academic publicationAI text detectionChatGPTethicsgenerative AIMDPIXGBoost

Identifiers

PMID40881186
PMCPMC12382442

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.