Most biomedical publications show signs of LLM-assisted writing

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Lena Holzwarth, Rita González-Márquez, Dmitry Kobak
• Venue: arXiv: cs.CL
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.10715v1
• PDF: https://arxiv.org/pdf/2608.10715v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

A study suggests that by the end of 2025, 89% of biomedical papers on PubMed Central showed an excess of language associated with LLM-assisted writing. The authors argue that this could impact future guidelines and policies.

What the Document Says

• Over the past several years, LLM-powered chatbots and agents have been widely used for academic writing.
• There are concerns about misconduct and fraud due to LLM-assisted writing.
• The study aims to monitor the prevalence of LLM-altered texts in scholarly publications.
• No existing method can produce reliable estimates. The authors propose a new unbiased approach based on changing word frequencies.
• The method was applied to full texts of open-access biomedical papers from PubMed Central.
• By the end of 2025, 89% of papers showed an excess of LLM-associated vocabulary.
• LLMs were twice as likely to be used in the Discussion section compared to the Methods section.
• Even inside the Methods section, the overall prevalence of LLM usage was over 50%.

Key Concepts Mentioned

• LLM-powered chatbots
• academic writing
• misconduct
• fraud
• PubMed Central
• word frequencies
• biomedical papers

Claims Made by the Document

• LLM-assisted writing is prevalent in academic writing.
• 89% of biomedical papers on PubMed Central showed an excess of LLM-associated vocabulary by the end of 2025.
• LLMs are twice as likely to be used in the Discussion section compared to the Methods section.

Evidence Used

• Widespread use of LLM-powered chatbots and agents for academic writing.
• Results from applying a new approach based on changing word frequencies to biomedical papers from PubMed Central.
• Statistical analysis of the frequency of LLM-associated vocabulary in different sections of the papers.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

The authors acknowledge that their method may not be able to detect all cases of LLM-assisted writing and that future research could further improve the approach.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The agent's understanding is limited by the provided text. For example, it cannot confirm if the study was peer-reviewed or what specific concerns about misconduct and fraud were present.

Open Questions

• What is LLM?
• How was the analysis of word frequencies conducted?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The study does not specify what LLM stands for.
• The text lacks information on how the word frequencies were analyzed and defined.

Raw Abstract / Excerpt

Over the past several years, LLM-powered chatbots and agents have become widely used as a tool for academic writing. LLM-assisted writing can be valuable by removing language barriers but at the same time causes concerns about misconduct and fraud. To inform policy decisions, it is necessary to monitor the prevalence of LLM-altered texts in scholarly publications. Despite some recent progress in this direction, no existing method can produce reliable estimates. Here we suggest and validate a new unbiased approach to estimate LLM usage in a corpus of texts based on changing word frequencies. We apply our method to the full texts of open-access biomedical papers from Pubmed Central, and show that by the end of 2025, 89% of papers show excess of LLM-associated vocabulary. We also find that LLMs are twice as likely to be used when writing a paragraph in the Discussion section (68%) compared to a paragraph in the Methods section (32%), but even inside the Methods section, the overall prevalence of LLM usage is over 50%. We believe that our estimates are crucial to shape future guidelines and policies.