The study in Cell Reports Physical Science said that a specialised classifier outperformed two existing artificial intelligence (AI) detectors and could help academic publishers to identify papers created by AI text generators.

University of Kansas chemist Heather Desaire said: "Most of the field of text analysis wants a really general detector that will work on anything. But by making a tool that focuses on a particular type of paper, "we were really going after accuracy."

Desaire and her colleagues first described their ChatGPT detector in June, when they applied it to Perspective articles from the journal Science.

Using machine learning, the detector examines 20 features of writing style, including variation in sentence lengths, and the frequency of certain words and punctuation marks, to determine whether an academic scientist or ChatGPT wrote a piece of text.

The findings show that "you could use a small set of features to get a high level of accuracy," Desaire said. The findings suggest that efforts to develop AI detectors could be boosted by tailoring software to specific types of writing.

Desaire said: "If you can build something quickly and easily, then it's not that hard to build something for different domains."