How Many Scientific Papers Are Written by AI? More Than You Think

Introduction

Large language models are already helping to write a measurable share of the scientific literature: one 2025 study estimates that at least 13.5% of biomedical abstracts published in 2024 were processed with an AI chatbot. This article looks at that evidence, why so much AI use goes undisclosed, why detection tools cannot settle the question, and what it means for the scholarly record.

Academic publishing has long been a mysterious black box. Papers are submitted, peer-reviewed, edited, and eventually published in prestigious journals, often after months or even years of back-and-forth revisions. For decades, this process has been viewed mainly as a human endeavor, driven by painstaking research, thoughtful analysis, and meticulous writing. But in recent years, something unexpected has crept into this scholarly ecosystem: artificial intelligence.

AI tools have rapidly evolved from clunky grammar checkers to sophisticated writing assistants, capable of churning out paragraphs of coherent, even compelling, text. While most academics are still learning to navigate these tools cautiously, a growing number are embracing AI-assisted writing. The implications are significant and somewhat unsettling. AI-generated text is already appearing in scientific papers at a rate far higher than most people realize. And no, this isn’t about minor tweaks to grammar or spelling. Algorithms are drafting entire sections, discussions, and conclusions.

This raises a rather uncomfortable question: Just how many scientific papers are written by AI? The answer, it seems, is “far more than you think.”

The Rapid Rise of AI in Academic Writing

It’s easy to dismiss AI in science writing as a fringe phenomenon, something limited to a handful of tech-savvy researchers playing with new toys. But the numbers suggest otherwise.

Hard numbers are now emerging. In July 2025, Dmitry Kobak and colleagues at the University of Tübingen published an analysis of 15.1 million PubMed abstracts in Science Advances. They tracked “excess vocabulary”, words such as “delves” whose use jumped after ChatGPT appeared, and estimated that at least 13.5% of abstracts published in 2024 had been processed with a large language model. Some published papers have even kept chatbot phrases or prompts in the text. And those are only the cases that leave a trace.

In reality, many more are likely using AI without disclosure. Researchers, after all, are under enormous pressure to publish, often juggling multiple projects alongside teaching, grant applications, and administrative duties. AI tools offer an irresistible shortcut. So, why spend hours agonizing over phrasing when an AI can draft it in minutes?

Attitudes are shifting too. A 2025 Nature survey of about 5,000 researchers found that more than 90% considered it acceptable to use generative AI to edit or translate a paper, and 65% thought it ethically acceptable to use it to write part or all of one, even though only a minority said they had actually done so. As tools tailored to biomedicine, physics and other fields mature, that gap between acceptance and use is likely to close.

It’s not hard to see why. AI can quickly generate literature reviews, summarize complex findings, or suggest improvements in clarity and flow. In some cases, it even identifies gaps in logic or missing citations, streamlining the writing process in ways that were previously unthinkable.

The Gray Zone of Authorship and Disclosure

One of the trickiest aspects of this AI revolution in science is the murky question of authorship. Many journals now require authors to disclose the use of AI tools in their submissions. Springer Nature, for example, has guidelines stating that large language models, such as ChatGPT, cannot be listed as authors; however, their use must be declared.

But here’s the catch: those guidelines rely on honesty. And let’s be frank, academia isn’t exactly known for its spotless record of transparency, especially in the face of relentless “publish or perish” pressure.

Even in cases where AI usage is disclosed, there’s little consistency in how it’s done. Some authors simply mention that ChatGPT was used for “editing and language polishing,” even when AI was likely involved in deeper conceptual writing. Others go as far as including entire AI-generated sections without labeling them as such, blending machine-generated prose with human text in a seamless, untraceable hybrid. We explain what a clear policy should cover in Writing an AI Policy for Authors.

The truth is that there’s currently no reliable way to detect AI-generated text in scientific literature, unless the authors openly disclose it. Detection tools, such as GPT detectors, remain inconsistent and easily fooled by paraphrasing or minor tweaks.

This puts the entire scholarly record at risk of becoming, well, filled with papers that were at least partially composed by machines, with no way for readers to know where the human ends and the AI begins.

Meet the AI Tools Quietly Transforming Scientific Writing

While ChatGPT dominates most headlines, it’s hardly the only AI tool making waves in academic writing. Other alternatives are quickly gaining traction, with some specifically designed for researchers.

Take Elicit, for example. This AI tool can assist researchers in conducting systematic reviews by automatically extracting key data from papers, organizing it into digestible tables, and even suggesting research directions. SciSpace, another rapidly growing platform, helps academics summarize scientific papers and generate citations in seconds. Then there’s Paperpal, designed to refine academic language and improve readability for non-native English speakers.

Even tools like Grammarly have quietly added AI-assisted rewriting features that go far beyond fixing typos. These tools can now restructure entire paragraphs, suggest transitions, and enhance academic tone, often producing text that sounds eerily polished.

The line between proofreading and ghostwriting has never been thinner.

Who’s Using AI in Academia? Hint: Pretty Much Everyone

AI in scientific writing isn’t just a niche phenomenon among early adopters. It’s spreading across disciplines and age groups. Junior researchers, often struggling with the overwhelming demands of academia, are among the most enthusiastic users. Younger researchers are significantly more likely to adopt these technologies than their older counterparts. But that generational gap is narrowing as AI tools become more widespread.

Field also plays a role. AI use is particularly high in computer science and engineering, where familiarity with machine learning tools is common. In the life sciences, adoption has been slower but is accelerating rapidly, especially as specialized tools emerge to handle domain-specific jargon and methodologies.

Interestingly, there’s also a geographical divide. Researchers in countries where English is not the primary language are more likely to rely on AI to help with manuscript preparation. In China, for instance, academic forums are filled with discussions about which AI tools produce the most “journal-acceptable” English text.

The Appeal and Danger of AI-Generated Science

Why are so many researchers turning to AI? The obvious answer is time. Drafting a scientific paper is a labor-intensive process. AI tools promise to shave hours, if not days, off this process.

But it’s not just about speed. Many researchers view AI as a democratizing force, leveling the playing field for those who struggle with English or academic writing conventions. AI tools can help non-native speakers avoid the stigma of poorly written prose, potentially improving their chances of acceptance in elite journals.

However, there are serious risks hiding behind this convenience. AI-generated text may sound convincing, but it often contains subtle inaccuracies, misinterpretations, or hallucinated references, which are fabricated citations to papers that don’t exist. These errors can sneak into published papers if authors aren’t careful, potentially polluting the scientific record with misleading or false information. Paper mills are exploiting the same tools at scale, as we describe in How Generative AI is Destroying the Credibility of Scientific Publishing.

Moreover, the widespread use of AI risks eroding essential academic skills. Writing isn’t just about stringing words together; it’s a crucial part of the research process, forcing scholars to clarify their thinking, organize their arguments, and critically evaluate their findings. Outsourcing this process to machines could dull these intellectual muscles.

Detection is a Losing Battle (For Now)

Faced with this growing tide of AI-generated academic writing, journals and publishers are scrambling to respond. Some have banned the use of AI-generated text altogether. Others have adopted more nuanced guidelines, allowing their use but requiring disclosure.

Still, the biggest obstacle to enforcing such rules is simple: detection remains unreliable.

AI detectors can sometimes catch obviously machine-generated text, but they struggle with more sophisticated outputs, especially when human editors tweak the text afterward. Tools like GPTZero and Turnitin’s AI detection module have shown mixed results in academic settings, and OpenAI withdrew its own AI text classifier in July 2023 because of its low accuracy.

And even if detection improves, it won’t necessarily solve the problem. Academic misconduct has always been a game of cat and mouse. As AI detection tools get better, so too will methods to evade them. Some researchers are already using paraphrasing tools to “humanize” AI-generated text, effectively laundering machine-written content into an undetectable form.

Are We Heading Toward an AI-Fueled Paper Boom?

The publishing industry has experienced a remarkable surge in submissions in recent years, with many attributing the initial wave of increased productivity to the COVID-19 pandemic. But some experts believe AI is now playing a key role in sustaining this flood of new papers.

The volume is already straining the system. Mark Hanson and colleagues, in their study The Strain on Scientific Publishing, found that the number of articles indexed in Scopus and Web of Science was about 47% higher in 2022 than in 2016, before chatbots were widely available. We look at the latest counts in More than 5 Million Scholarly Articles Were Published in 2024.

If AI-assisted writing and research automation keep spreading, annual output could keep climbing steeply. This could lead to an academic landscape where quantity vastly outweighs quality, further exacerbating issues like information overload and citation gaming.

Imagine a world where thousands of papers are churned out daily, many written largely by machines, flooding the scholarly record with a mix of valuable discoveries and algorithmically generated content. It’s not as far-fetched as it sounds.

Conclusion: The AI Genie Isn’t Going Back Into the Bottle

AI has already reshaped the way scientific papers are written, and there’s no going back. Tools like ChatGPT, Elicit, and Paperpal are now integral parts of many researchers’ workflows, speeding up the writing process, improving readability, and helping academics overcome language barriers.

But this convenience comes with trade-offs. The academic world must grapple with tough questions about authorship, integrity, and the very purpose of scholarly communication. Is it enough for a paper to sound polished and fluent, or does the writing process itself remain a crucial part of intellectual rigor? Can we trust a scientific record that increasingly blurs the line between human insight and machine output?

Ultimately, we may be entering a future where “human-authored” research becomes a niche category, a badge of intellectual craftsmanship in an AI-saturated landscape. For now, though, it’s safe to say that many of the papers appearing in journals today aren’t quite as human as they seem.

The question isn’t whether AI is writing scientific papers. It’s how many and just how much it matters.