JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) has advanced significantly in recent years, transforming many industries and improving the way people live and work. In scientific research, AI can enhance the quality and efficiency of data analysis and publication. However, AI has also opened up the possibility of generating high-quality fake papers that are difficult to detect, raising important questions about the integrity of scientific research and the trustworthiness of published papers.

Objective:

The aim of this study is to investigate the capabilities of current AI language models in generating high-quality fraudulent medical articles. We hypothesize that modern AI models can create highly convincing fraudulent paper that can easily deceive readers and even experienced researchers.

Methods:

The study used the GPT-3 language model to generate a fraudulent scientific article related to neurosurgery. GPT-3 is a large language model developed by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts given by users. The model was trained on a massive corpus of text from the internet and is capable of generating high-quality text in a variety of languages and topics. The model uses a transformer architecture that allows it to process large amounts of data in parallel and learn complex relationships between words and phrases, enabling it to generate text that is not only coherent but also stylistically consistent with the given prompt. The authors posed questions and prompts to the model, and refined them iteratively as the model generated the responses. Once the article was generated, it was reviewed for accuracy and coherence and compared to existing articles in the field.

Results:

The study found that the AI language model was capable of creating a highly convincing fraudulent paper that resembled a genuine scientific paper in terms of word usage, sentence structure, and overall composition. The article included standard sections such as Introduction, Material and Methods, Results, and Discussion, as well as tables and a chart. The generated manuscript consisted of 1992 words and 17 citations, and the whole process of article creation took approximately one hour without any special training of the human user. However, there were some concerns and specific mistakes identified in the generated article, specifically in references.

Conclusions:

The study demonstrates the potential of current AI language models to generate completely fabricated scientific articles. Although the papers look sophisticated and seemingly flawless, expert readers may identify semantic inaccuracies and errors upon closer inspection. We highlight the need for increased vigilance and better detection methods to combat the potential misuse of AI in scientific research. At the same time, it's important to recognize the potential benefits of using AI language models in genuine scientific writing and research, such as manuscript preparation and language editing.