We used the web-based chat interface ChatGPT (Chat Generative Pre-trained Transformer; OpenAI Limited Partnership), which relies on the GPT-3 (Generative Pre-trained Transformer 3) language model [5] to generate a scientific article related to the field of neurosurgery.

ChatGPT 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 on various 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.

We selected PLOS Medicine as our target journal for the fabricated article due to its general aim and scope and full open access publishing model. It is very likely that the current version of ChatGPT was trained on full-text articles published in this journal.

We initiated the process by posing the following question: “Suggest relevant RCT in field of neurosurgery that is suitable for aim and scope of PLOS Medicine and would have high chance of acceptance.” The reply was as follows:

We requested specific sections of the article, such as the abstract, introduction, methods, results, and discussion, along with appropriate citations. Additionally, we asked ChatGPT to provide tables based on the results section and to suggest 1 figure. As an AI language model, ChatGPT does not have graphic design capabilities, so it provided a data sheet, and we created a chart in Microsoft Excel without making any changes to the input data (see Chart 1 in Multimedia Appendix 1).

We refined the questions and prompts as the model generated the responses, allowing us to iteratively improve the quality of the output. The whole process of generating the article took no more than 1 hour.

The prompts were as follows:

Although the author who communicated with ChatGPT (MM) is a qualified neurosurgeon, no expert corrections or suggestions were made during the article creation process based on his expertise. Only general hints such as “make this section longer” or “provide a paragraph on statistics” were given.

Once we had generated a complete article, we reviewed it for accuracy and coherence, comparing it to existing articles in the field and consulting with domain experts (a psychiatrist and a statistician) to ensure that the content was relevant and accurate.

We also used ChatGPT to review the AI-generated article. The prompts were as follows:

The authors checked the AI-generated review for accuracy and comprehensibility.

We used publicly available web-based tools to identify AI-generated text. Specifically, AI Detector by Content at Scale [6] and AI Text Classifier by OpenAI [7] were used.

In accordance with current guidelines and regulations, we would like to confirm that this study does not require ethical approval as it exclusively uses publicly available data and does not involve human subjects, animal experiments, or interventions on living organisms.

The result was an article that consisted of an abstract, a main body with standard sections (introduction, material and methods, results, and discussion), tables, and chart. The final manuscript included 1992 words and 17 additional citations. Citations were in the correct format for PLOS Medicine. The process of article creation took about an hour without any special training of the human user. The whole fabricated manuscript is included as Multimedia Appendix 1.

A senior professor of neurosurgery (DN) reviewed the AI-generated article with the following remarks:

A board certified psychiatrist with interest in deep brain simulation (M Kasal) reviewed the AI-generated article with the following remarks:

A senior statistician with a medical degree (M Komarc) reviewed the AI-generated article with the following remarks:

The AI-generated review (Multimedia Appendix 2) gave quite accurate remarks regarding the fabricated article, pointed out strengths and weaknesses, and suggested possible changes. Despite the fact that some comments were self-evident (single-center study design and limited follow-up time), there were no substantial errors.

There are several publicly available web-based tools to identify AI-generated text. For example, AI Detector by Content at Scale claims to detect patterns and forecast the most probable word choices that lead to a higher AI detection probability [6]. We gave this tool a trial with our AI-generated article and the result was that probability of AI content was 48%, that is, inconclusive.

Another example of such a tool is AI Text Classifier by OpenAI (the same company that developed ChatGPT) [7]. AI Text Classifier gives result on a scale of very unlikely, unlikely, unclear if it is, possibly, or likely AI generated. Our AI-generated paper was classified as “unclear.”

AI Detector by Content at Scale found that the probability of AI content in the ChatGPT-generated review was 72%, that is, “highly likely to be AI generated.” AI Text Classifier by OpenAI classified the ChatGPT-generated review to be “likely AI generated.”

We have demonstrated that AI (ChatGPT) can create a highly convincing medical article that is completely fabricated with limited effort from a human user in a matter of hours. Nevertheless, the article would need an expert review and some improvements to be ready for possible submission. Shortcomings that are mentioned in the results section do not show any specific pattern; they are rather minor inaccuracies and minor study design flaws. Although a substantial number of citations seemed genuine at first glance, they were later found to have been fabricated. To our best knowledge, the errors the AI made were indistinguishable from those that a human could make.

There have been a number of high-profile cases of scientific fraud and misconduct in recent years, including cases where authors have fabricated or manipulated data, plagiarized content, or otherwise misrepresented their findings [11]. Although AI language models such as ChatGPT are a relatively new tool in scientific writing, it is possible that they could be used in similar ways to create fraudulent content.

ChatGPT is a cutting-edge NLP model developed by OpenAI that uses a transformer architecture to generate high-quality text in response to natural language prompts. Similar to other NLP models, ChatGPT works by analyzing large data sets of natural language text to learn patterns and structures in language, which it can then use to generate new text that is both coherent and contextually relevant.

At its core, ChatGPT is a large neural network that is trained on a massive corpus of text data (until the year 2021), such as books, articles, and web-based content. The model consists of multiple layers of self-attention and feedforward neural networks, which allow it to identify and model complex relationships between words and phrases in natural language text.

To generate new text using ChatGPT, a user provides a natural language prompt or question that the model uses to generate a sequence of tokens representing a coherent and contextually relevant response. The length and complexity of the response can be controlled by adjusting the parameters of the model, such as the length of the input prompt and the temperature of the sampling algorithm used to generate the response.

Although ChatGPT is primarily designed for use in conversational AI and chatbot applications, it has also shown promise in a range of other NLP tasks, including text completion, summarization, and machine translation. In recent years, researchers have also begun exploring the potential of ChatGPT and other NLP models for use in scientific writing and research, including generating scientific papers and summarizing research findings.

Some recent studies suggest that ChatGPT and other NLP models have substantial potential for use in scientific writing and research, particularly for tasks that involve summarizing or generating large volumes of text [12].

Some researchers point out that ChatGPT sometimes writes plausible sounding but incorrect or nonsensical answers and that using it for medical writing still requires human judgment [13]. However, our findings suggest that the level of sophistication required for human input may not be overly complex. An obvious weakness that we encountered in this study is the quality of citations. As technology continues to advance, it is likely that specialized large language models will be developed, reducing their monetary costs and mitigating some of their current limitations.

Interestingly, Kung et al [14] evaluated the performance of ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of 3 exams: Step 1, Step 2 Clinical Knowledge, and Step 3. ChatGPT performed at or near the passing threshold for all 3 exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations [14].

We are not aware of any specific evidence that ChatGPT has been intentionally misused for fraud in scientific writing, but it is certainly a possibility. Few articles have focused on abstract ghostwriting and its implications for the academic ethics of using AI in manuscript preparation, as well as issues of originality and authorship [15-17].

An obvious emerging challenge that publishers are facing is the detection of AI-created text. To address this challenge, many publishers are implementing various tools and techniques. One approach involves using machine learning algorithms to analyze the language, structure, and other features of the text to determine whether it was likely to have been created by a human or a machine. As demonstrated above, the current AI detection tools were unable to detect an AI-generated manuscript. However, in the case of an AI-generated review, these tools were more accurate, labeling the text as “likely” or “highly likely” to have been generated by AI. Another approach to address AI-generated content involves developing ethical guidelines and standards, which can help ensure that AI-generated content is transparent and accountable. Despite these challenges, the use of AI in scientific writing is likely to become increasingly common in the future, and publishers will need to continue to adapt and evolve their approaches to ensure the integrity and quality of their publications. An effective measure to prevent fraud as described in this paper (ie, completely fabricated articles) could be the mandatory submission of data sets, potentially verified by local authorities.

As we mentioned earlier, the ability of AI language models such as ChatGPT to generate coherent and realistic text has raised concerns about the potential for their misuse in creating fraudulent or misleading content. To the best of our knowledge, no paper has so far reported on fabricating a whole scientific article using AI.

In conclusion, our experiment using ChatGPT to generate an authentic looking but completely fabricated scientific paper highlights the potential risks associated with the use of AI in scientific writing. Although current AI language models can generate sophisticated and seemingly flawless papers, expert readers may identify semantic inaccuracies and errors upon closer inspection, particularly in the references.

As AI language models continue to advance in their capabilities, it will become increasingly important to develop ethical guidelines and best practices for their use in scientific writing and research. This may include strategies for verifying the accuracy and authenticity of content generated using these tools, as well as mechanisms for detecting and preventing fraud and misconduct.

At the same time, it is important to recognize the potential benefits of using AI language models in scientific writing and research, such as improving the efficiency and accuracy of document creation, analyzing results, and language editing. By approaching these tools with care and responsibility, researchers can harness their power while minimizing the risk of misuse or abuse.

Ultimately, the future of AI in scientific writing and research will depend on how well we navigate these ethical challenges and leverage the full potential of these tools for the benefit of scientific society.