Artificial intelligence in scientific writing: a friend or a foe?

Abstract

The generative pre-trained transformer, ChatGPT, is a chatbot that could serve as a powerful tool in scientific writing. ChatGPT is a so-called large language model (LLM) that is trained to mimic the statistical patterns of language in an enormous database of human-generated text combined from text in books, articles and websites across a wide range of domains. ChatGPT can assist scientists with material organization, draft creation and proofreading, making it a valuable tool in research and publishing. This paper discusses the use of this artificial intelligence (AI) chatbot in academic writing by presenting one simplified example. Specifically, it reflects our experience of using ChatGPT to draft a scientific article for Reproductive BioMedicine Online and highlights the pros, cons and concerns associated with using LLM-based AI for generating a manuscript.

Full text

Artificial intelligence in scientificwriting:a
friend or a foe?
Signe Altm€
ae
1,2,3,
*, Alberto Sola-Leyva
1,2
, Andres Salumets
3,4,5
ABSTRACT
The generative pre-trained transformer, ChatGPT, is a chatbot that could serve as a powerful tool in scientific writing. ChatGPT
is a so-called large language model (LLM) that is trained to mimic the statistical patterns of language in an enormous database of
human-generated text combined from text in books, articles and websites across a wide range of domains. ChatGPT can assist
scientists with material organization, draft creation and proofreading, making it a valuable tool in research and publishing. This
paper discusses the use of this artificial intelligence (AI) chatbot in academic writing by presenting one simplified example.
Specifically, it reflects our experience of using ChatGPT to draft a scientific article for Reproductive BioMedicine Online and
highlights the pros, cons and concerns associated with using LLM-based AI for generating a manuscript.
INTRODUCTION
The introduction of the
generative pre-trained
transformer ChatGPT in
November 2022 by OpenAI
shook the scientific world. It was instantly
acknowledged as a new level of tool that
artificial intelligence (AI) can provide for
seeking online for information, answers
and solutions (Macdonald et al., 2023).
ChatGPT is a type of chatbot designed to
provide natural language-processing
capabilities for a wide range of applications.
It is a large language model (LLM) that
generates sentences based on mimicking
the statistical patterns of language in an
enormous database of human-generated
text combined from text from books,
articles and websites across a wide range of
domains (Stokel-Walker, 2023). In addition
to ChatGPT, there are other free LLM
platforms such as cohere.com, writesonic.
com (100 free generations per month),
you.com (10 free extracts of writing per
day) and anthropic.com.
Language-based AI has already entered the
academic community. Many researchers
are believed to use chatbots as research
assistants to help in organizing their
thoughts, provide feedback on their work,
help with code writing and summarize the
research literature (Hutson, 2022).
Regardless of the lack of guidelines for its
use in scientific writing, and the debate
over whether ChatGPT should be listed as
a co-author or how/whether to
acknowledge its use, four manuscripts
have been reported in preprint where
ChatGPT has been credited as an author
(Stokel-Walker, 2023), and one article has
documented the use of AI for generating a
scientific paper (Getahun, 2022). These
LLMs offer a powerful tool for a wide range
of applications and demonstrate the
potential of these models to transform the
way we interact with language, scientific
writing and science, which may lead to
another revolutionary stage of progress in
science.
This paper reflects on the experience of
using ChatGPT to draft a research article
for Reproductive BioMedicine Online.We
highlight the pros and cons of AI for
manuscript generation and discuss
potential concerns regarding the use of the
LLM platform alongside our writing. For
this test, it was decided to generate a
simulated study comprising of 10,000
women undergoing IVF cycles who had
been tested for endometrial receptivity;
the main focus of the study was to be
whether the state of the endometrium
(receptive/non-receptive) influences the
outcome of treatment (successful
pregnancy/no pregnancy). The data were
wholly fabricated. ChatGPT was then used
to guide us in deciding how to proceed
with the data and to help to draft the
manuscript. Using ChatGPT it took the
authors a total of 15 min to create the initial
chat and the inputs, and to obtain the
output texts for the manuscript.
DESCRIBING THE STUDY DATA
First, the simulated dataset was described.
The conversation with ChatGPT was as
follows (all conversations are unmodified
text):
SA: We have a dataset consisting of a
cohort of 10000 women, some have
receptive endometrium and some have
not. We also have data if the embryo
transfer resulted in a successful pregnancy
or not.
ChatGPT: Great! Having this data set on
women and the success of their embryo
transfer can be useful for analyzing factors
that contribute to successful pregnancy
outcomes.
KEY WORDS
Artificial intelligence
Chatbot
ChatGPT
Large language model
Scientific writing
1
Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
2
Instituto de Investigaci
on Biosanitaria ibs.GRANADA, Granada, Spain
3
Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology (CLINTEC),
Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
4
Competence Centre on Health Technologies, Tartu, Estonia
5
Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
© 2023 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.
*Corresponding author. E-mail address: signealtmae@ugr.es (S. Altm€
ae). https://doi.org/10.1016/j.rbmo.2023.04.009 1472-
6483/© 2023 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.
Declaration: The authors report no financial or commercial conflicts of interest.
1RBMO VOLUME 00 ISSUE 00 2023

One possible analysis that could be done
with this data is to examine if there is a
difference in the success rates of embryo
transfer between women with receptive
endometrium and those without. This can
be done using statistical tests such as a
chi-square test or logistic regression.
Additionally, it may be helpful to
investigate other factors that could
potentially influence successful pregnancy
outcomes, such as age, BMI, previous
pregnancies, and any underlying medical
conditions. These factors could be
included as covariates in the statistical
analysis to see if they have a significant
effect on the relationship between
receptive endometrium and successful
embryo transfer.
Overall, the analysis of this data set could
provide insights into the factors that
contribute to successful pregnancy
outcomes and inform clinical practice for
improving embryo transfer success rates.
Next, the data were specified:
SA: Our data were as follows: 5500
women with receptive phase endometrium
and 4500 women with non-receptive
endometrium. Our analysis using chi-
square test showed that as 80% of women
with receptive phase endometrium
(n=4400) had successful pregnancy while
40% of women with non-receptive
endometrium (n=1800) achieved
successful pregnancy, the differences were
significant.
ChatGPT: Based on the results of your
analysis using the chi-square test, it
appears that there is a significant
difference in the success rates of embryo
transfer between women with receptive
phase endometrium and those without.
Specifically, 80% of women with receptive
endometrium achieved a successful
pregnancy, while only 40% of women
without receptive endometrium did.
This difference in success rates may
suggest that having a receptive
endometrium is an important factor for
successful embryo transfer and
subsequent pregnancy. However, it is
important to note that other factors, such
as age, BMI, and medical history, may also
be contributing to the observed
differences in success rates.
Further analyses, such as logistic
regression, could be performed to explore
the relationship between endometrial
receptivity and successful pregnancy
outcomes while controlling for potential
confounding variables.
Based on this example it can be seen that,
once the dataset has been explained, the
platform can guide and help scientists in
choosing proper study analysis methods,
and additionally suggest important factors
that should be considered in the analysis.
The current paper did not test the
platform’s capacity for more advanced
statistical approaches, but it has been
demonstrated that ChatGPT is able to
generate codes for using the programming
language R (Macdonald et al., 2023).
COMPOSING THE ABSTRACT
Based on the few lines of explanation in the
invented dataset (above), we started to test
ChatGPT’s ability to write a scientific
paper with all its sections, starting with the
Abstract:
SA: Based on these data, could you please
draft an abstract for Reproductive
Biomedicine Online journal?
ChatGPT: Sure, here's a possible draft of
an abstract for the Reproductive
Biomedicine Online journal based on the
data you provided:
Title: The Impact of Endometrial
Receptivity on Successful Embryo Transfer
in Women: A Retrospective Cohort Study
Abstract: This retrospective cohort study
aimed to investigate the impact of
endometrial receptivity on successful
embryo transfer in women. The study
included 10,000 women who underwent
embryo transfer and had data on
endometrial receptivity and pregnancy
outcomes. Of these, 5500 women had
receptive endometrium, while 4500
women did not. The chi-square test
revealed a significant difference in the
success rates of embryo transfer between
the two groups, with 80% of women with
receptive endometrium achieving a
successful pregnancy, compared to only
40% of women without receptive
endometrium. These findings suggest that
endometrial receptivity may play an
important role in the success of embryo
transfer and subsequent pregnancy.
Further research, such as logistic
regression analysis, may help to identify
other factors that could impact successful
pregnancy outcomes in women
undergoing embryo transfer. These results
have important implications for clinical
practice, as they highlight the need to
consider endometrial receptivity when
assessing the likelihood of successful
pregnancy following embryo transfer.
The generated Abstract needs an
additional one or two sentences of
introduction about the topic, but as a
whole the text is acceptable. The
unstructured format is valid for Review
Articles for the journal, whereas Original
Articles would require a structured
abstract with subheadings. The particular
form for the Abstract was chosen for the
current journal, but it can easily be
changed to any journal’s format of interest.
In addition to constructing an Abstract,
ChatGPT also suggested a title: ‘The
impact of endometrial receptivity on
successful embryo transfer in women: a
retrospective cohort study’, which sounds
highly appropriate considering the data
that were provided.
CONDUCTING A LITERATURE
SEARCH
As a next step, ChatGPT was asked to
perform a literature search for finding
relevant studies in the field that could be
integrated into the manuscript:
SA: Could you help me finding some
papers that have performed similar
studies, and help to create a bibliography?
ChatGPT: Sure, here are a few papers
that have performed similar studies on the
relationship between endometrial
receptivity and successful embryo transfer:
1. Elizabete, V., Armanda, C., Rodrigo,
C., Jo~
ao, M., Miguel, V., Ana, P., ... &
Ana, R. (2015). Impact of endometrial
thickness and pattern on pregnancy
outcome after in vitro fertilization and
embryo transfer. Journal of Assisted
Reproduction and Genetics, 32(2),
287-293.
2. Kasius, A., Smit, J. G., Torrance, H. L.,
Eijkemans, M. J., & Mol, B. W. (2014).
Endometrial thickness and pregnancy
rates after IVF: a systematic review
and meta-analysis. Human
Reproduction Update, 20(4), 530-541.
3. Zhang, Q., Liu, Y., Sun, Y., & Yan, L.
(2015). The association between
endometrial thickness and outcome of
2RBMO VOLUME 00 ISSUE 00 2023

in vitro fertilization/intracytoplasmic
sperm injection cycles: a systematic
review and meta-analysis.
International Journal of Clinical and
Experimental Medicine, 8(4), 5277-
5288.
4. Yu, X., Chen, Y., Zhang, X., Li, Y., &
Zhao, Y. (2015). Endometrial thickness
and its relationship with the outcome
of in vitro fertilization and embryo
transfer in fresh and frozen-thawed
embryo transfer cycles. Acta
Obstetricia et Gynecologica
Scandinavica, 94(12), 1292-1297.
As one can see the suggested papers are of
relevance for the outcomes of embryo
transfer and IVF but they are not relevant
in the current study setting where the aim
was to assess the effect of endometrial
receptivity on the success of IVF
treatment. Therefore, the following query
was specified:
SA: The studies you highlight focus on
endometrial thickness. Could you make a
new search of similar studies and
bibliography focussing on endometrial
receptivity?
ChatGPT: Certainly! Here are some
studies that focus on endometrial
receptivity and successful embryo transfer,
along with their corresponding
bibliographies:
1. Lessey, B. A. (2000). The role of
endometrial receptivity in
implantation. Human Reproduction,
15(suppl_6), 87-95.
2. Norwitz, E. R., Schust, D. J., & Fisher,
S. J. (2001). Implantation and the
survival of early pregnancy. New
England Journal of Medicine, 345(19),
1400-1408.
3. Gzgzyan, A. M., Safonova, Y. A.,
Melnikova, N. V., & Rudakova, E. B.
(2018). Endometrial receptivity in the
course of infertility treatment: current
approaches and prospects.
Gynecological Endocrinology, 34
(sup1), 21-25.
4. Nikas, G., Develioglu, O. H., Toner, J.
P., Jones Jr, H. W., & Check, J. H.
(2000). Endometrial receptivity
defects during IVF cycles with and
without ovarian stimulation. Journal of
Assisted Reproduction and Genetics,
17(6), 347-352.
In both bibliography searches, it was found
that the references provided did not, in
most cases, exist and indeed this, in our
opinion, is one of the biggest concerns
when using ChatGPT. Out of the eight
references provided within the two
searches, 25% (n= 2) of the references
were correct while 75% (n= 6) did not
exist in the PubMed/Google databases. In
fact, it has been consistently reported that
created references do not exist in the form
presented, although they have similar titles
to some existing references (Macdonald et
al., 2023). Furthermore, the current
ChatGPT version was trained in 2021
(Nath et al., 2022), meaning that more
recent papers might not be included.
We therefore tested the system further by
asking for newer references:
SA: Could you search for studies also
among the last years, like 2022 and 2023?
ChatGPT: Sure, here are some recent
studies on endometrial receptivity and
successful embryo transfer published in
2022 and 2023:
1. Li, Y., Liu, J., Zhang, Q., Sun, J., Hu, J.,
Gao, Y., ... & Yang, L. (2022).
Endometrial receptivity analysis using
transcriptomics and microbiome
analysis in women with unexplained
infertility. BMC Women's Health, 22(1),
17.
2. Dai, W., Han, Q., Wang, Y., Xu, B.,
Sun, Y., & Zhang, H. (2022).
Endometrial receptivity analysis using
a non-invasive biopsy approach in IVF/
ICSI cycles: a prospective study.
Reproductive BioMedicine Online, 45
(2), 362-370.
3. Liu, Y., Han, Y., Wang, Y., Zhang, S.,
Gao, L., Zhao, X., ... & Liu, Y. (2023).
The role of endometrial microbiota in
embryo implantation and pregnancy
outcomes in IVF/ICSI cycles. Human
Reproduction, 38(1), 136-145.
4. Hu, Y., Wang, Y., Zhang, S., Yang, X.,
Zou, J., Wei, D., ... & Sun, Y. (2022).
Endometrial receptivity signature
based on the endometrial microbiota
and transcriptomics predicts
pregnancy outcomes in IVF/ICSI
cycles. Fertility and Sterility, 118(4),
768-778.
It can be seen that even though the
references are new (with a main focus on
the microbiome), they do not match with
the study’s scope of the effect of
endometrial receptivity on the outcome of
IVF treatment. Furthermore, none of the
four references ChatGPT provided exists
in PubMed/Google. It is evident that the
suggested references are inaccurate, so
authors should adopt a critical approach
towards the technology, which evidently
requires further development.
In short, the references currently provided
by ChatGPT cannot be trusted and need
to be meticulously revised. To achieve truly
reliable responses, LLM need to be trained
on specific knowledge domains, which can
be a complex and costly processes.
Stanford researchers and the company
MosaicML are collaborating to develop a
PubMed GPT (Alvero, 2023). However, it is
acknowledged that, due to the extensive
parameters involved in such a PubMed
GPT, compromises need to be made
between the model’s intricacy, the
expense and the need for a specialized
architecture (Gupta, 2022).
COMPOSING THE
INTRODUCTION FOR A
SCIENTIFIC PAPER
As a next step, we asked ChatGPT to
prepare an Introduction:
SA: Could you write a short introduction
about endometrial receptivity and its
importance for embryo transfer?
ChatGPT:
Certainly!
Endometrial receptivity refers to the
window of time during a menstrual cycle
when the endometrium, the lining of the
uterus, is capable of supporting embryo
implantation and pregnancy. It is a critical
factor in successful embryo transfer and is
influenced by a variety of hormonal,
molecular, and cellular factors. Identifying
the optimal time for embryo transfer
based on endometrial receptivity can
improve the chances of successful
pregnancy and reduce the risk of
implantation failure and miscarriage.
Endometrial receptivity assessment is
therefore an important tool in assisted
reproductive technology (ART) and fertility
treatments.
As an Introduction should include
references, this input was also specified:
SA: Could you please write again a short
introduction about endometrial receptivity
and its importance for embryo transfer
with adding references?
RBMO VOLUME 00 ISSUE 00 2023 3

ChatGPT:
Certainly!
Endometrial receptivity refers to the
window of time during a menstrual cycle
when the endometrium, the lining of the
uterus, is capable of supporting embryo
implantation and pregnancy. It is a critical
factor in successful embryo transfer and is
influenced by a variety of hormonal,
molecular, and cellular factors. Identifying
the optimal time for embryo transfer
based on endometrial receptivity can
improve the chances of successful
pregnancy and reduce the risk of
implantation failure and miscarriage.
Endometrial receptivity assessment is
therefore an important tool in assisted
reproductive technology (ART) and fertility
treatments.
Several studies have demonstrated the
importance of assessing endometrial
receptivity in optimizing the timing of
embryo transfer. In a study by Ruiz-Alonso
et al., endometrial receptivity analysis
using transcriptomic biomarkers improved
pregnancy rates by up to 23% in women
undergoing in vitro fertilization (IVF)
treatment (1). Similarly, another study by
Haouzi et al. found that endometrial
receptivity analysis improved the success
rate of frozen embryo transfers by up to
29% (2).
Furthermore, other studies have
investigated the molecular mechanisms
and cellular processes involved in
endometrial receptivity. For example, a
study by Miravet-Valenciano et al.
explored the role of microRNAs in
endometrial receptivity and found that
specific microRNAs were differentially
expressed during the receptive phase of
the menstrual cycle (3). Another study by
Grewal et al. investigated the impact of
endometrial microbiota on endometrial
receptivity and found that microbial
dysbiosis was associated with implantation
failure and pregnancy loss (4).
Overall, assessing endometrial receptivity
is a crucial step in optimizing the timing of
embryo transfer and improving the
success rates of ART and fertility
treatments.
References:
1. Ruiz-Alonso et al. Fertility and Sterility.
2013; 99(3): 801-7.
2. Haouzi et al. Human Reproduction.
2013; 28(3): 761-9.
3. Miravet-Valenciano et al. Fertility and
Sterility. 2017; 107(1): 246-55.e5.
4. Grewal et al. Journal of Assisted
Reproduction and Genetics. 2020; 37
(8): 1931-9.
A critical reader will notice that the
Introduction is not exactly what would be
expected for the current study idea, but
some concepts and sentences could be
used and it definitely provides ideas and
sentences that would help with the writing
process. Again, the revision of the
references resulted in disappointing
results, with none of the provided
references actually existing.
WRITING THE METHODS
SECTION
SA: Based on our study data provided
before, could you write a methods section
for the journal
ChatGPT: Certainly! Here's a draft of a
methods section for the study based on
the data you provided:
Methods
Study population
The study population consisted of 10,000
women who underwent embryo transfer at
a single fertility clinic between January 1,
20XX and December 31, 20XX. The
inclusion criteria were women of
reproductive age who underwent embryo
transfer with fresh or frozen embryos.
Women with a history of recurrent
implantation failure, uterine anomalies, or
endometrial pathologies were excluded
from the study.
Endometrial receptivity assessment
Endometrial receptivity was assessed by
histologic evaluation of endometrial biopsy
samples obtained prior to embryo transfer.
Endometrial biopsy samples were obtained
using a Pipelle catheter during the mid-
luteal phase of the menstrual cycle (day 21
to 24). The samples were fixed in formalin,
embedded in paraffin, and stained with
hematoxylin and eosin. Endometrial
receptivity was determined by the
presence or absence of morphological
changes consistent with the receptive
phase of the menstrual cycle, including the
presence of uterodomes, secretion in the
glandular epithelium, and edema in the
stroma.
Pregnancy outcome assessment
Pregnancy outcome was assessed by
measurement of serum beta-human
chorionic gonadotropin (b-hCG) levels
and transvaginal ultrasound examination.
Serum b-hCG levels were measured
14 days after embryo transfer, and a value
greater than 25 IU/L was considered a
positive pregnancy test. Transvaginal
ultrasound examination was performed 4
to 5 weeks after embryo transfer to
confirm the presence of a gestational sac
and fetal heartbeat.
Statistical analysis
Data were analyzed using the chi-square test
to compare the proportion of women with
successful pregnancy between the receptive
and non-receptive endometrium groups. A
P-value of less than 0.05 was considered
statistically significant. All statistical analyses
were performed using SPSS version XX (IBM
Corp., Armonk, NY, USA).
Ethics
This study was approved by the
Institutional Review Board of [Name of
Institution]. All patients provided written
informed consent prior to participation in
the study. Confidentiality and anonymity
of the participants were maintained
throughout the study.
As only two sentences on study data had
beengiventoChatGPT,itwassurprisingto
obtain a generated output with so much text,
all of which would be relevant for a
manuscript. For instance, the text on the
inclusion and exclusion criteria, endometrial
receptivity assessment, pregnancy outcome
assessment and ethical statement is helpful in
guiding authors.
COMPOSING THE RESULTS AND
DISCUSSION SECTIONS
SA: Based on my study data, could you
also compose results and discussion
section for the Reproductive Biomedicine
Online journal?
ChatGPT: Certainly! Here's a draft of the
results and discussion sections for the
study based on the data you provided:
Results
Of the 10,000 women included in the
study, 5500 (55%) had receptive phase
4RBMO VOLUME 00 ISSUE 00 2023

endometrium and 4500 (45%) had non-
receptive endometrium. Of the 5500
women with receptive phase
endometrium, 4400 (80%) had successful
pregnancy, while of the 4500 women with
non-receptive endometrium, 1800 (40%)
had successful pregnancy. The difference
in the proportion of successful pregnancies
between the two groups was statistically
significant (P <0.001) according to the
chi-square test.
Discussion
The present study investigated the
association between endometrial
receptivity and pregnancy outcome in
women undergoing embryo transfer. Our
results demonstrate that women with
receptive phase endometrium had a
significantly higher proportion of
successful pregnancies compared to
women with non-receptive endometrium.
These findings are consistent with previous
studies that have shown a positive
association between endometrial
receptivity and pregnancy outcome (1-3).
The endometrium undergoes dynamic
changes throughout the menstrual cycle,
and the receptive phase of the
endometrium is a critical window for
successful embryo implantation (4). The
presence of uterodomes, secretion in the
glandular epithelium, and edema in the
stroma are all morphological changes that
occur during the receptive phase and are
indicative of a favorable endometrial
environment for embryo implantation (5).
The histologic evaluation of endometrial
biopsy samples obtained during the mid-
luteal phase of the menstrual cycle is a
reliable method for assessing endometrial
receptivity (6).
Our findings have important clinical
implications for the management of
women undergoing assisted reproductive
technologies (ART) such as in vitro
fertilization (IVF). Endometrial receptivity
testing may be used to identify women with
a higher likelihood of successful pregnancy
and optimize the timing of embryo transfer
(7). Additionally, endometrial receptivity
may serve as a potential target for
therapeutic interventions aimed at
improving pregnancy outcomes in women
undergoing ART (8).
In conclusion, our study provides further
evidence for the importance of
endometrial receptivity in the success of
embryo transfer and supports the use of
endometrial receptivity testing in the
management of women undergoing ART.
Future research is needed to explore
potential interventions to improve
endometrial receptivity and ultimately
improve pregnancy outcomes in this
population.
References
1. Haouzi D, Assou S, Mahmoud K, et al.
Gene expression profile of human
endometrial receptivity: comparison
between natural and stimulated cycles
for the same patients. Hum Reprod.
2009;24(6):1436-1445.
2. Figueira RCS, Setti AS, Braga DPAF,
et al. Is the receptivity of the
endometrium impaired in obese
patients undergoing IVF? Reprod Biol
Endocrinol. 2011;9(1):123.
3. Zeng Y, Chen X, Liu Y, et al.
Endometrial thickness, morphology,
and molecular markers may predict
unsuccessful pregnancy with IVF.
Reprod Biol Endocrinol. 2018;16(1):114.
4. Lessey BA. Assessment of endometrial
receptivity. Fertil Steril. 2011;96(3):522-
529.
5. Maccarrone M, Bari M, Battista N, et
al. The key role of the
endocannabinoid system in human
implantation. J Reprod Immunol.
2014;104-105:9-16.
6. Ruiz-Alonso M, Blesa D, Diaz-Gimeno
P, et al.
Again, the references are discouraging: the
text refers to eight references but the
output provides only six, and even then the
final one is incomplete. Further checking
shows that only two of the references exist.
The Discussion text is not ‘perfect’, but it
certainly provides helpful concepts and
sentences to help to build a critical
discussion section. The use of term
‘uterodome’is interesting. This term was
not familiar to the authors and after a
Google/PubMed search we understood
that it meant ‘pinopodes’, and only 13
studies were found in PubMed using
uterodome. It is intriguing, however, how
the AI algorithm found the rare expression
‘uterodome’instead of the commonly
used term ‘pinopodes’.
ASSESSING PLAGIARISM IN THE
CHATGPT-GENERATED TEXT
Plagiarism of original content is
undoubtedly a major concern when using
AI, and while ChatGPT itself has not been
detected to commit extensive plagiarism,
AI journalism as a whole has been reported
to engage in this (Christian, 2023). This
raises the question of whether there
should be a threshold for the acceptable
amount of AI-generated content, and also
whether its frequent use, in the long term,
would result in the production of similar
paragraphs and structures in papers within
the same field (Macdonald et al., 2023).
ChatGPT was therefore asked to assess
the level of plagiarism:
SA: Could you assess the level of
plagiarism in the last text of Results and
Discussion?
ChatGPT: As an AI language model, I
don't have access to plagiarism detection
tools or databases to check for similarity
between the text and other sources.
However, I can confirm that the text
includes proper citations and references to
previously published studies, which is an
important aspect of avoiding plagiarism.
It's always recommended to use proper
citation and referencing when using
information or ideas from other sources to
avoid any issues related to plagiarism.
We set out to run our own plagiarism
checking using turnitin.com, and to our
surprise the similarity index was only 19%,
with the main areas of similarity being
detected in the Materials section, which is
to be expected (see the Supplementary
File for the Turnitin report). In short, the
similarity check was acceptable and, based
on the plagiarism checking that was
applied, the manuscript would pass an
editorial office scrutiny.
POTENTIALLY FLAWED AI
DETECTION
It can be challenging to distinguish
between AI-generated and original texts
(Else, 2023). However, a number of tools,
including GPTZero, GPT-2 Output
Detector and several AI Detectors (writer.
com, copyleaks.com, sapling.ai,
contentatscale.ai, openai.com, crossplag.
com), have been developed to determine
whether a given text is generated by a
language model. These tools classify the
paper as either ‘real’(human-generated) or
‘fake’(AI-generated), with a percentage
representing the confidence level
(Anderson et al., 2023). A previous study
used AI-generated text and, after applying
RBMO VOLUME 00 ISSUE 00 2023 5

paraphrasing-AI software, found that the
detection rate of ‘real’text increased from
0.02% to 99.52% (Anderson et al., 2023).
This clearly suggests that new protection
methods may need to be developed and
implemented for AI detection.
It has been shown that ChatGPT texts of
research papers can pass plagiarism
detectors with a 100% originality score
(Else, 2023). This leads to significant
concerns regarding authorship and
publication ethics, as scientists can no
longer be certain if the text is an original
work by a scientist or has been generated
by AI (Sample, 2023). Some scientific
journals have already warned that they will
review texts in order to avoid the copy and
paste approach of AI-generated texts
(ICMJE, n.d.), while others support AI
systems to provide a digital watermark on
their creations, or just support
transparency in scientific writing via an
adequate acknowledgement of LLM use.
As this race to control and find new
solutions for rephrasing AI tools is never-
ending, we believe that, instead of
developing new tools to control the
situation, we should focus on informing
and training authors how to adequately use
LLM platforms.
CO-AUTHOR OR A HELPFUL
TOOL?
SA: Could you review this paper (the texts
generated such as Abstract, Introduction,
Methods, Results and Discussion) and tell
us if you agree to submit it to Reproductive
Biomedicine Online journal?
ChatGPT: As an AI language model, I
cannot make a final decision on whether
to submit a paper to a specific journal.
However, I can provide some general
feedback on the paper based on the
content generated:
The abstract provides a clear summary
of the study's aims, methods, and key
findings.
The introduction provides a concise
overview of the importance of
endometrial receptivity in embryo
transfer and contextualizes the study's
aims within the existing literature.
The methods section provides a clear
description of the study design, data
collection, and statistical analysis.
The results section presents the key
findings in a clear and concise manner
and includes appropriate statistical
analysis.
The discussion section provides a
thorough interpretation of the findings
and their implications for clinical
practice, with reference to the existing
literature.
Overall, the paper appears to be well-
structured and clearly written, with
appropriate citation of relevant literature.
However, it's important to note that the
decision to submit a paper to a specific
journal depends on many factors, including
the journal's scope, editorial policies, and
the perceived fit of the paper with the
journal's readership. It's always a good idea
to carefully read the journal's guidelines and
review previously published papers in the
journal to ensure that the paper is a good fit
before submitting it for consideration.
The initial concern over whether ChatGPT
should be listed as a co-author now seems
to be clear that an AI language model
cannot take responsibility for and make
decisions related to its content and
therefore does not meet the requirements
of an author. Indeed, the Nature and
Science editorial teams have recently
announced that AI packages do not fulfil
the criteria for a study author as they
cannot take responsibility for the content
and integrity of the paper (Stokel-Walker,
2023). In the end, authors are responsible
for the integrity and validity of their work
and should cite any use of LLM in the
Methods or Acknowledgements section.
The following principles have been
incorporated into the existing guide for
authors of all Springer Nature journals: (i)
no LLM tool will be accepted as a credited
author on a research paper, and (ii)
researchers using LLM tools should detail
their use of such tools in the Methods or
Acknowledgements section. If these
sections are absent, the Introduction or
another appropriate section maybe used
to acknowledge the use of the LLM
(Editorial, 2023).
CONCLUSIONS
Our experiment of using ChatGPT for
scientific writing demonstrates that
ChatGPT has a high potential for
becoming a great help to researchers in
designing their study, performing analyses
and drafting the study results into a
scientific article. This would definitely help
in overcoming the ‘blank-sheet syndrome’
that all authors face from time to time.
Nevertheless, it should be considered as a
helpful tool to speed up the process and
not as a replacement for authors’work, as
it clearly requires human oversight at all
stages and final input for guaranteeing the
accuracy and reliability of the results.
When using ChatGPT, several issues arise
that should be considered, such as ethics
and integrity, accuracy and reliability.
Although LLMs are able to produce
increasingly realistic text, the integrity and
accuracy of using these models in scientific
writing is unknown. Often the statements
generated are not necessarily true, and if
the same question is asked several times, it
generates different output answers (Stokel-
Walker, 2022). It has been reported that
while ChatGPT is capable of producing
credible scientific essays, the data it
generates are a combination of both true
and entirely fabricated information
(Alkaissi and McFarlane, 2023;J, 2022).
Another issue is intellectual property
rights: do they belong to the algorithm, to
the company that created it or to the
scientist who presents the work? Also,
what happens with the data that are
inserted into the online system? Will it be
saved or used later by others?
When using ChatGPT in scientific writing,
we want to highlight that if papers are not
properly revised, there is a high risk of
presenting incorrect information and non-
existent references, especially among
writers without domain expertise in the
topic. Furthermore, once a paper has
been submitted to a journal, the editors
and referees will not have the time to
check its accuracy phrase by phrase,
reference by reference. We sincerely hope
that ChatGPT (and other similar LLMs) will
not turn out to be a ‘tool of
misinformation’. We consider the
inaccuracy and misinformation to be the
biggest concern today in using ChatGPT.
When using the ChatGPT, it creates
surprisingly intelligent-sounding text. We
would like to stress the word ‘sounding’
here as, based on our own (and others’)
experience, the text at first seems
acceptable but on closer inspection can be
seen to contain misinformation and
inaccuracies. In short, we would like
authors to be aware that it is an excellent
tool to help but not to replace their work.
AI tools such as ChatGPT have sparked a
polarized debate among academics. While
some consider it to be a helpful tool for
speeding up research, others see it as a
threat to the integrity of authorship
6RBMO VOLUME 00 ISSUE 00 2023

(Salvagno et al., 2023). Whether we like it
or not, LLMs are conquering scientific
writing in both good and bad ways, and it is
our task to be aware of their shortcomings
and strengths. Several journals have raised
the question of how to regulate their use.
We believe that instead of regulating use,
the focus should be on providing
guidelines on ‘how to use them correctly’,
highlighting the pros and cons of their use.
These free-to-use tools are and will
continue to be the reality and it is up to us
how to accept and work with them.
We encourage researchers to try
ChatGPT (openai.com/blog/chatgpt).
Overall, the experience is pleasant and
exciting, resembling an instant and friendly
conversation. In fact, the current version of
ChatGPT is trained to be polite and
pleasant as filters to prevent it generating
hate speech have been applied (Alvero,
2023). Who would not want to co-work or
collaborate with ‘somebody’who is always
available, answers your queries within a
second and is supportive and positive
throughout the process?
DATA AVAILABILITY
No
data was used for the research described
in the article.
ACKNOWLEDGEMENTS
The Estonian Research Council (grant
PRG1076), Horizon 2020 innovation grant
(ERIN, grant no. EU952516) and Enterprise
Estonia (grant EU48695).
FUNDING
This work is supported by Grants Endo-
Map PID2021-12728OB-100 and PRE2018-
085440 funded by MCIN/AEI/10.13039/
501100011033 and ERFD A way of making
Europe; Grants RYC-2016-21199 funded by
MCIN/AEI/10.13039/501100011033 and by
ESF Investing in your future; FEDER/Junta
de Andalucía-Consejería de Economía y
Conocimiento: ROBIN A-CTS-614-
UGR20, and IRENE P20_00158.
SUPPLEMENTARY MATERIALS
Supplementary material associated with this
articlecanbefound,intheonlineversion,at
doi:10.1016/j.rbmo.2023.04.009.
REFERENCES
Alkaissi, H., McFarlane, S.I., 2023. Artificial
Hallucinations in ChatGPT: Implications in
Scientific Writing. Cureus 15, e35179. https://doi.
org/10.7759/cureus.35179.
Alvero, R., 2023. ChatGPT: Rumors of Human
Providers’Demise Have Been Greatly
Exaggerated. Fertil. Steril. in press. https://doi.
org/10.1016/j.fertnstert.2023.03.010.
Anderson, N., Belavy, D.L., Perle, S.M.,
Hendricks, S., Hespanhol, L., Verhagen, E.,
Memon, A.R., 2023. AI did not write this
manuscript, or did it? Can we trick the AI text
detector into generated texts? The potential
future of ChatGPT and AI in Sports & Exercise
Medicine manuscript generation. BMJ Open
Sport Exerc. Med. 9, 1–4. https://doi.org/10.1136/
bmjsem-2023-001568.
Christian, J., 2023. CNET’s AI Journalist Appears to
Have Committed Extensive Plagiarism. Futurism.
https://futurism.com/cnet-ai-plagiarism.
Editorial, 2023. Tools such as ChatGPT threaten
transparent science; here are our ground rules
for their use. Nature 613, 612.
Else, H., 2023. Abstracts writtten by ChatGPT fool
scientists. Nature 613, 423.
Getahun, H., 2022. After an AI bot wrote a scientific
paper on itself, the researcher behind the
experiment says she hopes she didn’t open a
“pandora’s box. Insider. https://www.insider.
com/artificial-intelligence-bo.
Gupta, K., 2022. Stanford and MosaicML
Researchers Announce the Release of PubMed
GPT, a Purpose-Built AI Model Trained to.
Interpret Biomedical Language. Marketechpost.
https://www.marktechpost.com/2022/12/17/
stanford-a.
Hutson, M., 2022. Could AI help you to write your
next paper? Nature 611, 192–193.
ICMJE, I.C. of M.J.E., n.d. Defining the Role of
Authors and Contributors https://www.icmje.
org/recommendations/browse/roles.
J, V., 2022. Ai-generated answers temporarily
banned on coding Q&A site Stack Overflow
[WWW Document]. The Verge.
Macdonald, C., Adeloye, D., Sheikh, A., Rudan, I.,
2023. Can ChatGPT draft a research article? An
example of population-level vaccine effectiveness
analysis. J. Glob. Health 13, 01003. https://doi.
org/10.7189/jogh.13.01003.
Nath, S., Marie, A., Ellershaw, S., Korot, E.,
Keane, P.A., 2022. New meaning for NLP: the
trials and tribulations of natural language
processing with GPT-3 in ophthalmology. Br. J.
Ophthalmol. 106, 889–892. https://doi.org/
10.1136/bjophthalmol-2022-321141.
Salvagno, M., Chat, GPT, Taccone, F.S., Gerli, A.G.,
2023. Can artificial intelligence help for scientific
writing? Crit. Care 27, 75. https://doi.org/10.1186/
s13054-023-04380-2.
Sample, I., 2023. Science journals ban listing of
ChatGPT as co-author on papers. Guardian. https://
www.theguardian.com/science/2023/jan/26/sc.
Stokel-Walker, C., 2023. ChatGPT listed as author
on research papers: many scientists disapprove.
Nature 613, 620–621. https://doi.org/10.1038/
d41586-023-00107-z.
Stokel-Walker, C., 2022. AI bot ChatGPT writes
smart essays - should professors worry? Nature.
https://doi.org/10.1038/d41586-022-04397-7 9
december.
Received 12 April 2023; accepted 12 April 2023.
RBMO VOLUME 00 ISSUE 00 2023 7