The paper explores the widespread use of large language models (LLMs) in academic writing, particularly focusing on the impact of ChatGPT. Using a large-scale, unbiased approach, the authors analyze 14 million PubMed abstracts from 2010 to 2024 to track changes in vocabulary usage. They find that the introduction of LLMs led to a significant increase in the frequency of certain style words, indicating that at least 10% of 2024 abstracts were processed with LLMs. This usage varies across disciplines, countries, and journals, with some sub-corpora showing up to 30% LLM usage. The authors compare this to the impact of major events like the Covid-19 pandemic, suggesting that the effect of LLMs on scientific writing is unprecedented. They also discuss the implications of LLM usage, including potential issues such as factual errors, bias, and the production of fake publications. The study highlights the need for policies and regulations to address these challenges in scientific publishing.The paper explores the widespread use of large language models (LLMs) in academic writing, particularly focusing on the impact of ChatGPT. Using a large-scale, unbiased approach, the authors analyze 14 million PubMed abstracts from 2010 to 2024 to track changes in vocabulary usage. They find that the introduction of LLMs led to a significant increase in the frequency of certain style words, indicating that at least 10% of 2024 abstracts were processed with LLMs. This usage varies across disciplines, countries, and journals, with some sub-corpora showing up to 30% LLM usage. The authors compare this to the impact of major events like the Covid-19 pandemic, suggesting that the effect of LLMs on scientific writing is unprecedented. They also discuss the implications of LLM usage, including potential issues such as factual errors, bias, and the production of fake publications. The study highlights the need for policies and regulations to address these challenges in scientific publishing.