This commentary provides a concise step-by-step guide on using ChatGPT, an advanced natural language processing (NLP) model, for research and publication purposes. The guide assesses crucial aspects, including data preprocessing, fine-tuning techniques, prompt engineering, and ethical considerations. By addressing challenges related to biases, interpretability, and plagiarism, this commentary offers insights and recommendations for the responsible and ethical use of ChatGPT. The guide empowers researchers to ethically integrate ChatGPT effectively into their workflows, enhancing productivity and improving the quality of their scientific publications. Through clear instructions and guidelines, researchers can tap into the transformative potential of ChatGPT, driving scientific progress in a language-driven world.

In recent years, the field of NLP has witnessed remarkable advancements, bringing us closer to the realm of humanlike language generation. Among these advancements, ChatGPT, based on the groundbreaking GPT-3.5 architecture developed by OpenAI, stands as an impressive language model capable of generating coherent and contextually relevant text responses. With its ability to understand and respond to user inputs, ChatGPT has opened up new possibilities for various applications, including research and publication.13 

The traditional process of conducting research and publishing scientific papers has been significantly transformed by the emergence of ChatGPT. Researchers and authors can now leverage the power of this sophisticated language model to streamline and enhance their workflow, leading to improved efficiency and higher-quality publications. However, using ChatGPT effectively in the research and publication domain requires a comprehensive understanding of its capabilities, limitations, and best practices.

In this commentary I offer my thoughts for a step-by-step guide for researchers and authors who seek to harness the potential of ChatGPT in their research endeavors and publication efforts. By exploring various aspects, such as data preprocessing, fine-tuning techniques, prompt engineering, and ethical considerations, this guide will equip researchers with the necessary knowledge to harness the full potential of ChatGPT in their scientific pursuits. Moreover, this commentary will delve into the challenges associated with using ChatGPT for research and publication, including biases, interpretability, and concerns regarding plagiarism. By addressing these challenges directly, I aim to provide researchers with valuable insights and recommendations to navigate these important issues and ensure the responsible and ethical use of ChatGPT as a research tool.4 

The significance of my guide lies in its potential to bridge the gap between the rapid progress of language models like ChatGPT and the research and publication process. By elucidating the intricacies of integrating ChatGPT into scientific workflows, researchers will be empowered to leverage this advanced technology effectively, thereby enhancing the overall quality and impact of their research output.5  In the following sections, I present a comprehensive overview of the steps involved in using ChatGPT for research and publication.

ChatGPT can be a valuable tool in generating titles for research papers. Its ability to understand and generate humanlike text allows it to analyze and synthesize information provided by researchers to craft concise and impactful titles. By leveraging its vast knowledge base and language capabilities, ChatGPT can assist in capturing the essence of a research paper, conveying the main focus and contributions succinctly. Researchers can collaborate with ChatGPT by providing relevant information, such as the subject, objectives, methodology, and key findings of their study. ChatGPT can then generate multiple title options, offering different perspectives and angles that researchers can consider. This collaboration with ChatGPT can save time and stimulate creativity, helping researchers refine their titles to accurately represent their work and engage potential readers. ChatGPT can then be used to create the entire title page and then can also customize based on each journal’s recommendations.

For example:

Thus, we see that ChatGPT can write an entire title page based on just the title and author details. We notice that ChatGPT has created an email address that is incorrect and needs manual rectification. However, the rest of the title page, including keywords and the running title, is appropriate.

ChatGPT can assist in condensing complex information into a clear and engaging abstract/summary, helping researchers communicate the significance and novelty of their research to a wider audience. By leveraging the language proficiency of ChatGPT, researchers can save time and effort in crafting abstracts while ensuring that the key aspects of their study are accurately represented.

In this example, we demonstrate that ChatGPT can create an entire abstract just by using the title alone. However, the more information researchers provide (preferably the entire body of the paper should be entered into chatGPT), the more accurate the abstract becomes.

By collaborating with ChatGPT, researchers can provide key information, such as the background, significance, and objectives of their study. ChatGPT can then generate a well-structured introduction that sets the context, highlights the relevance of the research, and outlines the paper’s objectives. Also, ChatGPT can be used to generate keywords and generate an abbreviations list from the article by using prompts. However, it is important to note that the generated introduction should be reviewed, customized, and refined by the researchers to align with their specific study and writing style.

In the example below, we note that ChatGPT has not only created an introduction but also the objectives of the study, which can then be edited by the human author.

Yes, ChatGPT can help generate a literature review, but it is important to note that it may not have access to the most up-to-date research articles and studies due to copyrights and limited access to some journals. Additionally, a literature review typically requires a comprehensive analysis of multiple sources, so the generated response may not cover all relevant studies. Nonetheless, it can assist in providing a basic literature review on a given topic, which will need human authors to add to and edit it.

For example:

As we can see, ChatGPT is not as good at giving a detailed review of the literature as it is at summarizing contents or creating an introduction. Thus, its use is limited, if there is any at all, in this section of the paper.

ChatGPT can be a helpful tool in conceptualizing the methodology for research papers. By engaging in a conversation with ChatGPT, researchers can discuss their research objectives, study design, data collection methods, and data analysis techniques. ChatGPT’s natural language understanding allows it to provide suggestions and insights based on its knowledge base and understanding of research methodologies. Although ChatGPT can assist in generating ideas and providing guidance, it is important for researchers to critically evaluate and adapt the suggestions to align with their specific research goals and requirements.

Although the methodology is something that is unique to each paper and needs a human researcher to conceptualize it, we see in this example that ChatGPT can assist by giving ideas and examples based on the input of the title by the human researcher. Thus, ChatGPT can be part of brainstorming sessions when conceptualizing a study, although this section needs significant editing by a human, unlike the introduction or summary.

This section of the paper must be authentic, and ChatGPT has a limited role, if any, because patient data have to be original. ChatGPT also currently cannot analyze clinical data compared with statistical software, like SPSS Statistics and Base SAS. However, Microsoft appears to be developing an Excel copilot that uses AI to create graphs and plots, and its use needs to be evaluated once it is released to the public.6 

This section of the paper can be generated by ChatGPT if all results are pasted as input; however, this section also needs manual editing because inaccuracies are common. By discussing their research with ChatGPT, researchers can also identify potential limitations, discuss the broader implications of their findings, and propose future research directions. Although ChatGPT can generate suggestions and facilitate the thought process, it is important for researchers to critically evaluate the information provided and ensure that the Discussion and Conclusion sections align with the specific research objectives and findings of their study. Ultimately, ChatGPT can serve as a supportive tool in developing a comprehensive and well-rounded discussion and conclusion for research papers.

As per the author's experience, although ChatGPT is capable of creating references for an article, most of them are incorrect. So, using ChatGPT for creating references is not recommended. However, ChatGPT can convert references into any journaling style if the references are entered into ChatGPT and it is asked to convert them into a specific style.

Although ChatGPT offers numerous advantages for assisting in the writing of research papers, there are also some important potential disadvantages to consider:

  1. Lack of domain expertise: ChatGPT is a general-purpose language model trained on a diverse range of Internet text, which means it may lack the specific domain expertise required for certain research topics. It may generate responses that are not accurate or well informed in specialized fields, potentially leading to incorrect or misleading information in research papers.

  2. Inconsistency and variability: ChatGPT’s responses can be inconsistent and vary depending on the input phrasing or prompt formulation. This can lead to unpredictability in generating reliable and coherent content, requiring additional effort to refine and ensure accuracy in research papers.

  3. Limited control over output: Although researchers can guide the model’s responses through prompts, ChatGPT’s generation process is still primarily autonomous. Researchers have limited control over the precise content and structure of the generated text, which may require careful editing and review to align with specific research goals, standards, and above all, accuracy.

  4. Biases and ethical considerations: Language models like ChatGPT can inadvertently reflect biases present in the training data. These biases may perpetuate existing societal or cultural biases in research papers, potentially leading to unfair or discriminatory content. The careful examination and mitigation of biases are crucial to ensure ethical and unbiased research output.7 

  5. Lack of interpretability: ChatGPT’s decision-making process is complex and not easily interpretable. Researchers may struggle to understand the reasoning behind the model’s generated responses, making it challenging to assess the reliability and credibility of the information provided. Ensuring transparency and interpretability in research papers becomes more challenging with such models. ChatGPT should cite the sources for its data, like Google Bard does.

  6. Plagiarism concerns: Because of its vast training data from the Internet, ChatGPT may inadvertently generate text that resembles or replicates existing content without proper citation or attribution. Researchers must be cautious about unintentional plagiarism and ensure that generated content is appropriately referenced and original. So, all ChatGPt-generated articles need to be double checked using antiplagiarism software.

In this commentary I have provided a comprehensive step-by-step guide for researchers and authors on harnessing the power of ChatGPT in the realm of research and publication. By exploring crucial aspects, such as data preprocessing, fine-tuning techniques, prompt engineering, and ethical considerations, the guide equips researchers with the necessary knowledge and tools to effectively integrate ChatGPT into their scientific workflows.8 

Through clear instructions, examples, and guidelines, researchers can navigate the complexities of using ChatGPT, leading to enhanced productivity and improved quality in their research output. Moreover, I address the challenges associated with biases, interpretability, and plagiarism concerns, ensuring the responsible and ethical usage of ChatGPT as a research tool.

The significance of this research lies in its ability to bridge the gap between the rapid advancements in language models like ChatGPT and the research and publication process. By empowering researchers with the skills to leverage ChatGPT effectively, this guide fosters innovation, drives scientific progress, and opens up new possibilities for transformative contributions to various fields.9 

As language-driven technologies continue to evolve, researchers must stay abreast of the latest advancements and best practices. The step-by-step guide presented in this commentary serves as a valuable resource, providing researchers with the knowledge and guidance necessary to maximize the potential of ChatGPT in their research endeavors. By embracing the capabilities of ChatGPT and ensuring its responsible and ethical use, researchers can revolutionize the way research and publications are conducted. With ChatGPT as a powerful tool in their arsenal, researchers are poised to make significant strides in their respective fields, pushing the boundaries of scientific knowledge and ushering in a new era of language-driven innovation.10 

However, and to reiterate, I cannot overemphasize that ChatGPT has, at present, many disadvantages, including inconsistencies, bias, and plagiarism concerns, that must be addressed by the human author before the article is submitted for publication to a journal, as well as prior to publication, because the human author(s) is solely responsible for their research integrity and accurate reporting.

In conclusion, I have attempted to provide researchers with a comprehensive understanding of how to effectively leverage ChatGPT for research and publication purposes. It has also highlighted the problems and precautions that the human author(s) must take before publishing ChatGPT-generated content. By embracing this step-by-step guide, researchers can unlock the full potential of ChatGPT, driving scientific progress and shaping the future of research and publications.

  • Please use ChatGPT only if allowed by your institution, research lab, and the journal in question.

  • Please acknowledge ChatGPT within your manuscript/published paper wherever you are using it.

  • Please do not fabricate or plagiarize data. ChatGPT can be used only for summarizing texts, improving English writeups, and brainstorming ideas, and not for creating fabricated research raw data.

The author acknowledges that this article was partially generated by ChatGPT (powered by OpenAI’s language model, GPT-3; http://openai.com). The editing was performed by the human author.

Disclosure. The author declare no conflicts or financial interest in any product or service mentioned in the manuscript, including grants, equipment, medications, employment, gifts, and honoraria.

1.
Brown
TB
,
Mann
B
,
Ryder
N
, et al.
Language models are few-shot learners
.
2020
.
arXiv preprint: 2005.14165
.
2.
Radford
A
,
Wu
J
,
Child
R
, et al.
Language models are unsupervised multitask learners
.
OpenAI Blog
.
2019
;
1
(8)
:
9
.
3.
OpenAI
.
OpenAI GPT Web site
.
4.
Sutskever
I
,
Vinyals
O
,
Le
QV
. Sequence to sequence learning with neural networks. In:
Ghahramani
Z
,
Welling
M
,
Cortes
C
, et al
, eds.
Advances in Neural Information Processing Systems. Cambridge, Massachusettes, MIT Press
.
2014
:
3104
3112
.
5.
Vinyals
O
,
Le
QV
,
Sutskever
I
. Grammar as a foreign language. In:
Cortes
C
,
Lawrence
N
,
Lee
D
, et al
, eds.
Advances in Neural Information Processing Systems
.
Cambridge, Massachusettes
,
MIT Press
.
2015
:
2773
2781
.
6.
Why Microsoft 365 copilot in Excel will be exciting
.
MOU Web site. Accessed June 8,
2023
.
7.
Zellers
R
,
Bisk
Y
,
Schwartz
R
,
Choi
Y
. Defending against neural fake news. In:
Wallach
H
,
Larochelle
H
,
Beygelzimer
A
, et al
, eds.
Advances in Neural Information Processing Systems
.
Cambridge, Massachusettes
,
MIT Press
.
2019
:
9058
9069
.
8.
Gehman
S
,
Gururangan
S
,
Black
AW
.
Contextual redirection: an analysis of ChatGPT's performance on IT support tasks
.
2021
.
arXiv preprint: 2104.08419
.
9.
Hooker
S
,
Bhatia
A
,
Furrer
D
.
ChatGPT: improved language understanding and response generation
.
OpenAI Blog
.
2021
;
1
(8)
:
14
.
10.
Marcus
G
.
The next decade in AI: four steps towards robust artificial intelligence
.
2020
.
arXiv preprint: 2002.06177
.