Open Access Original
Article

DOI: 10.7759/cureus.49019

ChatGPT-4 and Human Researchers Are Equal in
Writing Scientific Introduction Sections: A
Blinded, Randomized, Non-inferiority Controlled
Study

Review began 09/12/2023
Review ended 11/14/2023
Published 11/18/2023© Copyright 2023
Sikander et al. This is an open accessarticle distributed under the terms of theCreative Commons Attribution License CC-

Binyamin Sikander 1 , Jason J. Baker 1 , Can D. Deveci 1 , Lars Lund 2 , Jacob Rosenberg 1
1. Surgery, Herlev Hospital, Herlev, DNK 2. Urology, Odense University Hospital, Odense, DNK

BY 4.0., which permits unrestricted use,
distribution, and reproduction in any
medium, provided the original author andCorresponding author: Binyamin Sikander, binyamin2200@gmail.com

source are credited.

Abstract
Background
Natural language processing models are increasingly used in scientific research, and their ability to perform
various tasks in the research process is rapidly advancing. This study aims to investigate whether Generative
Pre-trained Transformer 4 (GPT-4) is equal to humans in writing introduction sections for scientific articles.

Methods
This randomized non-inferiority study was reported according to the Consolidated Standards of Reporting
Trials for non-inferiority trials and artificial intelligence (AI) guidelines. GPT-4 was instructed to synthesize
18 introduction sections based on the aim of previously published studies, and these sections werecompared to the human-written introductions already published in a medical journal. Eight blindedassessors randomly evaluated the introduction sections using 1-10 Likert scales.

Results
There was no significant difference between GPT-4 and human introductions regarding publishability andcontent quality. GPT-4 had one point significantly better scores in readability, which was considered a nonrelevant difference. The majority of assessors (59%) preferred GPT-4, while 33% preferred human-written
introductions. Based on Lix and Flesch-Kincaid scores, GPT-4 introductions were 10 and two points higher,
respectively, indicating that the sentences were longer and had longer words.

Conclusion
GPT-4 was found to be equal to humans in writing introductions regarding publishability, readability, andcontent quality. The majority of assessors preferred GPT-4 introductions and less than half could determine
which were written by GPT-4 or humans. These findings suggest that GPT-4 can be a useful tool for writing
introduction sections, and further studies should evaluate its ability to write other parts of scientific articles.

Categories: Medical Education, Healthcare Technology
Keywords: natural language processing, chatbot, artificial intelligence and writing, artificial intelligence in medicine,
gpt-4, chatgpt

Introduction
Writing high-quality scientific articles is essential when communicating research findings to the scientificcommunity. Introduction sections of scientific articles play an important role in providing the necessarycontext, identifying the research gap, and establishing the aim [1]. Artificial intelligence (AI) is rapidly
advancing, leading to the development of AI systems that perform a wide range of tasks in the research
process [2,3]. Specifically, natural language processing models such as Chat Generative Pre-trained
Transformer (ChatGPT) and the newest model GPT-4, developed by OpenAI (San Francisco, CA) [4], havebeen utilized in various applications. These models mimic human-like conversations and provide
appropriate responses. They are regularly enhanced with reinforcement techniques and machine learning to
improve their understanding and responsiveness to users' inquiries. You can ask GPT-4 anything and receive
human-like replies to your questions or requests [5]. This includes manuscript writing [6], and assessment
for grammar, spelling, and style [7]. While there is a growing interest in using AI-synthesized text for
academic purposes [8], AI-synthesized text could be a potential tool for researchers in the development of
their articles [3]. We hypothesized that GPT-4 could write scientific introduction sections just as well as
human researchers regarding publishability, readability, and content quality.
This study investigated whether GPT-4 and humans were equal in writing scientific introduction sections ina randomized blinded non-inferiority design.

How to cite this article
Sikander B, Baker J J, Deveci C D, et al. (November 18, 2023) ChatGPT-4 and Human Researchers Are Equal in Writing Scientific Introduction
Sections: A Blinded, Randomized, Non-inferiority Controlled Study. Cureus 15(11): e49019. DOI 10.7759/cureus.49019

Materials And Methods
This blinded, randomized, non-inferiority study was reported according to the Consolidated Standards ofReporting Trials extension for non-inferiority and equivalence randomized trials (CONSORT non-inferiority)
and artificial intelligence (CONSORT-AI) [9,10]. The study was conducted within our research network, andno changes were made to the method after the study’s commencement. The 18 latest articles published in"The Lancet eClinicalMedicine," an open-access journal that covers all medical specialties, were selected tohave their introduction sections reproduced using GPT-4. In the pilot phase, we found that using one
command for all study types was ineffective, so we limited our analysis to the 18 latest comparative original
studies. The study consisted of two parallel groups: one group with human-written introductions fromalready published articles and another group with introductions synthesized by GPT-4. Both introduction
sections had the same aim of study. The 18 pairs of introduction sections were distributed amongst eightblinded assessors, each receiving six to seven pairs of introductions in a random sequence, ensuring thateach pair would be evaluated three times. The blinded assessors who judged the introductions were either
novel- or non-users of ChatGPT and were required to have both a Doctorate of Medicine (DM) and Doctor ofPhilosophy (Ph.D.) and published at least four articles as first author, and they were recruited byconvenience sampling in our research network. The GPT-4-synthesized introductions comprised ofbackground, research gap, and aim for a scientific journal. The GPT-4 was fed with the aim from the alreadypublished original article and was instructed to “Write an introduction section including background,research gap, and aim for a scientific journal with the aim ‘Insert aim’” followed by a request to “make thebackground more detailed.” The GPT-4-synthesized introduction sections were altered in text size and font
to match the originals, but no changes were made to language or words (Supplementary File 1). We removedreferences from the original articles as GPT-4's output does not automatically include references, and also
since this was beyond the scope of this study.
The primary outcome was to compare the GPT-4-synthesized with human-written introduction sections
regarding publishability, readability, and content quality using 1-10 Likert scales. A questionnaire wasdeveloped for the blinded assessors to evaluate the introduction sections (Supplementary Files 2-4). To
ensure the questionnaire's design, several pilot tests were conducted. Additionally, the questionnaire wasface-validated with persons who potentially could be included as assessors (but not participating in the
study) to ensure the understanding and interpretation of each question [11]. After reviewing the comments
provided following face-validation, the author group made minor adjustments to the questionnaire. A scoreof 10 on the Likert scale indicated that no changes were needed for publishability, readability, or adequate
detail for content quality, while a score of 1 signified unpublishable or unreadable content or improper detaillevel. For content quality scores below 10, additional questions were asked to identify whether there was alack of details, excessive details, or a combination of both.
The secondary outcome involved an assessment of the introduction’s sections, whereby assessors wereasked to indicate their preferences and identify whether the introductions were generated by GPT-4 orhumans, providing their reasoning in free text. The free-text answers were grouped into similar answer
options. An objective language analysis was conducted to compare the readability of GPT-4-synthesized andhuman-written introductions. This was done using online calculators for Lix [12,13] and Flesch-Kinkaid[14,15] scoring systems for readability evaluating factors such as sentence length, number of words, andnumber of letters per word.

Sample size
The sample size calculation was based on continuous data from a Likert scale ranging from one to 10, using
an online calculator [16]. The least relevant difference was set to one point on the Likert scale. The
calculation was based on the formula n = f (α, β) × 2 × σ2 /d2, where the significance level (alpha) was set at
5%, the statistical power (1-beta) at 90%, the standard deviation of the outcome (σ) was set as 1, and thenon-inferiority limit (d) was set at 1. This indicates that GPT-4-synthesized introductions could beconsidered non-inferior if they were ≤1 point worse or better. This resulted in a sample size of 18 samples
per group.

Randomization and blinding
An online tool [17] was used to generate a randomized allocation sequence for the distribution and sequenceof the introduction section pairs to the blinded assessors. This ensured variation in the order of exposure of
GPT-4 and human-written introduction sections. Each pair of introduction sections was numbered from one
to 18 and assigned as either number 1 or 2 for allocation concealment. Only the first author knew the orderof randomization.

Statistical analyses
Statistical analyses were conducted with IBM SPSS Statistics for Windows (version 28.0., IBM Corp., Armonk,
NY), and graphic illustrations with Microsoft Excel for Windows 2023 (version 16.72, Microsoft Corporation,
Redmond, WA). Normal distribution was assessed with Shapiro-Wilk tests, histograms, and Q-Q plots. Since
most continuous data were non-normally distributed, all data were analyzed with the Mann-Whitney U test
to uniformly present data. Descriptive statistics for continuous data were presented as median and range.

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Categorical data were presented as crude rates and compared with the chi-squared test. In the noninferiority design, a difference of ≤1 point in the Likert scale was considered as not relevant regarding
publishability, readability, and content quality. Since each introduction section was independently assessedby three assessors, we used the Fleiss' kappa method to determine the inter-rater agreements and thereby
investigate the consistency of the assessments. Fleiss' kappa was chosen because it accounts for chance anddoes not assume that the same assessors rated all items [18]. According to the Landis and Koch scale, weclassified inter-rater agreement as follows: very good (>0.8-1.0), good (>0.6-0.8), moderate (>0.4-0.6), fair
(0.2-0.4), and poor (<0.2) [19].

Ethics
According to the Danish legislation, approval from the ethics committee was not required [20]. Additionally,
Danish Data Protection Agency approval was not required as personal data were not processed in the project.

Results
Table 1 presents the results of the assessment of GPT-4-synthesized and human-written introduction
sections by the blinded assessors. It shows the median scores and ranges for publishability, readability, andcontent quality. The 18 pairs of introduction sections were assessed three times, resulting in a total of 54
assessments. There was no significant difference between GPT-4 and human introductions in terms of
publishability and content quality. However, GPT-4 had a significantly higher score on readability, but thedifference was less than one on the Likert scale, which was considered a non-relevant difference.

Assessment

GPT-4

HumansP-value

Publishability, median (range)

9.0 (3–10]

8.5 (1–10)

0.061

Readability, median (range)

9.0 (2–10]

8.5 (1–10)

0.010

Content quality, median (range)

9.0 (2–10]

9.0 (1–10)

0.119“Too superficial,” n (%)

13 (24)1 (2)“Too detailed,” n (%)

9 (17)

21 (39)“Lacking/too detailed in some areas,” n (%)

11 (20)11 (20)“No need for change,” n (%)

21 (39)21 (39)Flesch-Kincaid, median (range)

20 (16–22)

18 (14–21)

0.013

Lix score, median (range)

72 (61–76)

62 (53–78)

0.002

Evaluation criteria

Readability test

TABLE 1: Summary of assessment
The eight blinded participants' assessment of 18 Generative Pre-trained Transformer 4 (GPT-4) synthesized vs. 18 human-written published introductions.
Each introduction section was assessed three times, resulting in an assessment of 54 (3 x 18) introduction sections in each group. The level of detail forcontent quality is presented in quotation marks. n: number; %: percentages. The table shows the average and range for Likert scales, reflecting thecollective opinions of respondents. A higher average indicates stronger agreement. The Lix score measures text complexity based on sentence length,
while the Flesch-Kincaid score assesses readability based on word and sentence length. Lower scores suggest easier readability.

Most of the assessors preferred the GPT-4 introductions (59%, n = 32), while 33% (n = 18) preferred thehuman-written introductions, and 8% (n = 4) had no preference (Figure 1). This difference in preference
between GPT-4 and human introductions was significant, meaning there was a clear distinction in people's
preferences (*P < 0.02). Among those who preferred GPT-4 introductions, the free-text comments revealed
that 50% (n = 16) chose them because of "better flow and formulation," 41% (n = 13) preferred them for being"short and concise," and 9% (n = 3) believed that they better expressed "details according to the aim." Whenthe human-written introductions were preferred, 72% (n = 13) reported preferring them due to "morerelevant background information and GPT-4 being too superficial," 17% (n = 3) reported "better flow andformulation," and the remaining 11% (n = 2) preferred them due to "better structure."

2023 Sikander et al. Cureus 15(11): e49019. DOI 10.7759/cureus.49019

3 of 10FIGURE 1: The blinded assessors’ preferences of either the Generative
Pre-trained Transformer 4 (GPT-4) or human-written introductions
The difference between the assessment for GPT-4 and human introductions was significant (p < 0.02).

Figure 2 presents the assessors' guesses of whether they thought the introduction sections were written by
GPT-4 or humans. A total of 44% (n = 24) guessed correctly, and the remaining 56% (n = 30) could either not
differentiate or assumed incorrectly. Among those who guessed correctly, 63% (n = 15) stated that GPT-4
introductions were “too superficial and less factual” compared with the human-written introductions, while
37% (n = 9) stated that the “formality in the language and generic textbook introduction structure” could tell
the two apart. Of those who guessed correctly, there was an equal 50% (n = 12) distribution between thepreference for GPT-4 and human-written introductions.

FIGURE 2: The blinded assessors’ guess on whether the introduction
section was Generative Pre-trained Transformer 4 (GPT-4) or humanwritten
The difference between the assessment for GPT-4 and human introductions was significant (p < 0.02).

The calculated Fleiss' kappa for all three parameters expressed poor inter-rater agreement amongst thethree blinded assessors. The kappa coefficients and P-values were as follows: (k) = 0.059 (CI 95%: -0.34 to
0.152) and P = 0.212 for publishability; (k) = -0.007 (CI 95%: -0.098 to 0.084) and P = 0.887 for readability;and (k) = 0.081 (CI 95%: -0.008 to 0.170) and *P = 0.073 for content quality.
According to Flesch-Kincaid's grade levels and Lix scores (Table 1), the introduction sections synthesized by2023 Sikander et al. Cureus 15(11): e49019. DOI 10.7759/cureus.49019

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GPT-4 had significantly higher scores for readability, indicating that they should have been harder to readcompared with the human-written introductions. However, in the Flesch-Kincaid grading level, bothhuman-written and GPT-4-synthesized introductions were assessed as “college graduate level” in terms ofdifficulty. The Lix scores also indicated that they were in the same category “Very hard” in terms ofdifficulty. The difference between the GPT-4-synthesized and human-written introductions was two points
on the Flesch-Kincaid grade level and 10 points on the Lix score level, which represents a 10% difference on
both scales [12,14].

Discussion
This study found that GPT-4-synthesized introductions were equally as good as human-written
introductions, with no significant differences in terms of publishability and content quality. While GPT-4
had a significantly better readability score, this small difference was not considered relevant. Furthermore,
the introduction sections created by GPT-4 were frequently preferred over the human-written introductions.
Less than half of the assessors correctly guessed which introduction sections were written by GPT-4 orhumans. The remaining majority was distributed almost evenly among those who were not able todifferentiate or guessed incorrectly. Finally, when comparing the introduction sections with the FleschKincaid and Lix scores, the readability scores for GPT-4-synthesized introductions were significantly higher
than those for human-written introduction sections. The objective readability tests showed that text written
by GPT-4 had longer sentences and words. We could have told GPT-4 to make them shorter, but the assessors
found the GPT-4 written text easier to read, indicating that it was unnecessary.
GPT-4 is a useful tool for various research writing tasks, including generating drafts for research protocols,
manuscripts, grant proposals, and patient education materials [21]. In the grouped responses for the
preference for human introductions, a proportion reported that the information provided by GPT-4 was “toosuperficial.” However, a matching proportion of the grouped responses that preferred the GPT-4
introductions reported that the content was short and well-connected to the aim. This leaves the decision
inconclusive as to whether GPT-4 can produce content that would saturate the needs of a scientific article.
Nonetheless, we believe that GPT-4 should be seen as an assisting tool to improve the overall quality of thecontent produced by researchers. Furthermore, GPT-4 can improve the quality of existing text and eliminate
errors [21,22]. This is supported by our findings that the majority of assessors preferred the GPT-4synthesized introductions over the human-written introductions. Approximately 50% of the reasoning for
GPT-4 being preferred over human-written introductions was due to “better flow and formulation” in termsof the writing.
No previous study has compared the readability scores between GPT-4 and human-written text throughobjective means of scoring (Flesch-Kincaid and Lix). However, in the subjective assessment of readability,
GPT-4 scored higher than human-written introductions across the 18 introduction pairs for comparison. The
median difference between GPT-4 and human-written text was both relevant and statistically significant for
both scoring tools as the 10% difference indicates a different grading of readability. The major reason for
preferring the GPT-4-synthesized introductions over the human-written ones was due to “better flow andformulation” referring to better readability of the text. These differences between the subjective andobjective assessment may be due to how the scoring tools calculate readability, based on the length ofsentences and words providing a grade or score that indicates objective readability [12,14]. However, theymight not be usable in this context as they do not consider the flow and formulation of language.
The strengths of this randomized and blinded non-inferiority study include reporting according to
CONSORT non-inferiority and AI guidelines [9,10] and the validation of the questionnaire [11]. The interrater agreement was poor, but the bias from the subjective assessment was reduced because each pair of
introduction sections was reviewed by three different assessors, thus increasing the reliability of theassessments. We conducted subjective and objective readability assessments to get a comprehensive
overview of the GPT-4 and human introduction sections. The assessors had a minimum of a Ph.D. degree toensure a suitable research background. This study also had some limitations such as the setting of thestandard deviation at 1, which could potentially underestimate the true variability and complexity of thedata. The inclusion of introduction sections from a single journal focused on comparative studies may limit
generalizability. However, the journal from which the introduction sections were acquired covers a broad
variety of medical specialties, such as cardiology, neurology, immunology, psychiatry, and gastroenterology,
which enhances generalizability. Furthermore, the command given to GPT-4 was simple and based on thegeneral format for introductions and the aim of the originally published articles. Assessors were recruited
through convenience sampling, ensuring no interaction between participants and blinding the distributionof introduction sections. Previous concerns regarding GPT-4's association with the research process stemfrom the lack of validity in its data output [22].
AI is increasingly integrated into our daily lives, expanding its presence in both the researcher [22,23] andclinicians' daily workflow [24,25]. Our study revealed that researchers with a Ph.D. degree or higher rated
GPT-4 introductions as non-inferior to human-written published introduction sections. GPT-4 willundoubtedly impact various stages of the research process in the near future, and maintaining transparency
in academic research to uphold ethical standards is therefore a priority [3]. GPT-4 is applicable in assisting
researchers in crosschecking introduction sections for suggestions to improve readability without affecting

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content quality or publishability [21,22]. Thus, it has the opportunity to increase the effectiveness and time
consumption in the writing process of scientific articles. Future research should focus on the potential of AI
and related risks to incorporate GPT-4 and other AI models in the research process. Future studies could also
investigate the use of AI tools to assist in other areas of the scientific research process.

Conclusions
In conclusion, GPT-4 was rated as at least equal to humans in writing introduction sections in terms of
publishability, readability, and content quality. Most of the assessors preferred the GPT-4 introductions dueto their better flow and formulation, being short and concise, and presenting details according to the aim of
the studies. In contrast, human-written introductions were preferred by some, due to more relevant
background information and better structure. The Lix and Flesch-Kincaid scores were significantly higher for
introductions written by GPT-4, indicating that they should have been more difficult to read. However, thiswas not the case in the subjective assessment of readability, probably due to better flow and structure. The
findings in this study suggest that GPT-4 can be a valuable tool in writing introduction sections, but the use
would require research-based background knowledge to ensure the validity of the output.

Appendices
Supplementary file 1
Prompt: "Write an introduction section including background, research gap, and aim for a scientific journal with the aim."
Answer: The study aimed to analyze kidney function trends before and after COVID-19-related AKI in a large, hospitalized cohortand to identify factors linked to mortality and impaired long-term kidney recovery. The findings will enhance comprehension of
COVID-19-associated AKI and inform targeted interventions for improved patient care. The coronavirus disease 2019 (COVID-19)
pandemic has posed unprecedented challenges to global healthcare systems, with acute kidney injury (AKI) emerging as asignificant complication among hospitalized patients. Although the prevalence and impact of COVID-19-associated AKI havebeen widely documented, the precise temporal trajectories of kidney function preceding and following this condition remain
poorly understood. A comprehensive understanding of these trajectories is crucial for delineating the pathophysiologicalmechanisms underlying COVID-19-associated AKI and for devising effective management strategies. Despite the growing bodyof literature on COVID-19-associated AKI, there exists a critical research gap in the characterization of the temporal dynamics ofkidney function in the context of this specific condition. Furthermore, while short-term outcomes of COVID-19-associated AKI
have been investigated, there is a paucity of data on the long-term clinical sequelae, particularly focusing on mortality andimpaired kidney function recovery post-hospitalization. In light of these gaps, this study aimed to comprehensively characterize
the temporal trajectories of kidney function both preceding and following COVID-19-associated AKI in a large cohort of
hospitalized patients. Additionally, we aimed to identify and analyze the clinical factors associated with mortality and impaired
long-term kidney function recovery in this specific population. By addressing these objectives, we sought to provide valuable
insights into the pathophysiology of COVID-19-associated AKI and contribute to the development of targeted interventions
aimed at optimizing patient care and long-term outcomes in this vulnerable population. Prompt: "Make the background moredetailed" Answer: The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis, profoundly affecting
healthcare systems worldwide. Among the multifaceted complications associated with COVID-19, acute kidney injury (AKI) has
emerged as a significant concern, contributing to the complexity of patient management and outcomes. Various studies havehighlighted the prevalence and impact of COVID-19-associated AKI, revealing its association with heightened morbidity,
prolonged hospital stays, and increased mortality rates. However, despite the growing awareness of this complication, thedetailed temporal dynamics of kidney function before and after the onset of COVID-19-associated AKI have not been fully
elucidated. A thorough understanding of the temporal trajectories of kidney function in the context of COVID-19-associated AKI
is imperative for comprehensively unraveling the underlying pathophysiological mechanisms governing this intricate interplay.
The progression of kidney dysfunction in the lead-up to AKI, as well as the subsequent recovery or deterioration of renal
function post-AKI, remain areas that require in-depth investigation. Gaining insight into these trajectories can shed light on thecritical windows of vulnerability and potential avenues for early intervention and management. Furthermore, while existing
literature has primarily focused on short-term outcomes, there exists a notable research gap regarding the long-term clinicalsequelae of COVID-19-associated AKI. Understanding the factors influencing both mortality rates and impaired long-term kidney
function recovery among COVID-19 patients who have experienced AKI is essential for establishing comprehensive prognostic
indicators and refining patient care strategies. Identifying these determinants can aid in risk stratification and facilitate thedevelopment of tailored interventions aimed at optimizing long-term renal health and overall patient outcomes. Given these
critical gaps in the current knowledge base, the primary objective of this study was to conduct a comprehensive characterizationof the temporal trajectories of kidney function preceding and following COVID-19-associated AKI in a substantial cohort of
hospitalized patients. In tandem, the study sought to discern the clinical factors intricately linked with both mortality andimpaired long-term kidney function recovery in this specific patient population. The findings of this study are anticipated toprovide crucial insights into the pathophysiology of COVID-19-associated AKI and offer a foundation for the development oftargeted interventions to enhance patient care and optimize long-term outcomes in this vulnerable patient cohort.

TABLE 2: Example of prompts for creating a scientific introduction section given to GPT-4

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Supplementary file 2 (questionnaire)

FIGURE 3: Questions regarding introduction sections written by either ahuman or ChatGPTSupplementary file 3 (questionnaire)

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7 of 10FIGURE 4: Questions regarding introduction sections written by either ahuman or ChatGPTSupplementary file 4 (questionnaire)

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8 of 10FIGURE 5: Questions regarding introduction sections written by either ahuman or ChatGPT
Preference and assessment.

Additional Information
Disclosures
Human subjects: All authors have confirmed that this study did not involve human participants or tissue.
Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.
Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare thefollowing: Payment/services info: All authors have declared that no financial support was received fromany organization for the submitted work. Financial relationships: All authors have declared that they haveno financial relationships at present or within the previous three years with any organizations that might
have an interest in the submitted work. Other relationships: All authors have declared that there are noother relationships or activities that could appear to have influenced the submitted work.

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