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Artificial intelligence in academic writing: a paradigm-shifting technological advance

Authors:
Roei Golan at University of Florida
Roei Golan
Rohit Reddy at University of Miami
Rohit Reddy
Akhil Muthigi at Georgetown University
Akhil Muthigi
Ranjith Ramasamy at Jumeirah American Clinic
Ranjith Ramasamy
  • Jumeirah American Clinic
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Abstract

Artificial intelligence (AI) has rapidly become one of the most important and transformative technologies of our time, with applications in virtually every field and industry. Among these applications, academic writing is one of the areas that has experienced perhaps the most rapid development and uptake of AI-based tools and methodologies. We argue that use of AI-based tools for scientific writing should widely be adopted.
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nature reviews urology
https://doi.org/10.1038/s41585-023-00746-x
Comment
Artificial intelligence in academic writing:
a paradigm-shifting technological advance
Roei Golan, Rohit Reddy, Akhil Muthigi & Ranjith Ramasamy
Articial intelligence (AI) has rapidly become
one of the most important and transformative
technologies of our time, with applications in
virtually every eld and industry. Among these
applications, academic writing is one of the
areas that has experienced perhaps the most
rapid development and uptake of AI-based
tools and methodologies. We argue that use
of AI-based tools for scientic writing should
widely be adopted.
The use of artificial intelligence (AI) in academic writing can be divided
into two broad categories: those that assist authors in the writing pro-
cess; and those that are used to evaluate and assess the quality and
validity of written work. Tools such as natural language processing
that can understand and generate human-like language can assist
authors in writing and preparing manuscripts (Box 1). Tools such as
plagiarism detection software and automated peer-review platforms
can assist reviewers and editors in evaluating the quality of the manu-
script. Furthermore, automated peer-review platforms can quickly
and objectively evaluate large numbers of manuscripts, potentially
reducing their workload1.
One of the most considerable advantages of AI-based tools in
academic writing is that they can save time and improve efficiency.
For example, natural language processing algorithms can help
authors to identify and correct errors in their work, enabling them
to focus on the content of their writing rather than on mechanics.
These algorithms also can be used to assist with tasks such as lan-
guage translation and text summarization, which can save time and
improve the efficiency of thorough literature reviews. Furthermore,
natural language processing algorithms can generate specific out-
lines for manuscripts, research protocols, grant proposals, informed
consents, emails, insurance letters of medical necessity, reports and
other written documents. These outlines can be used as frameworks
to ensure that crucial components of a text are included. Lastly, lan-
guage processing can assist with strengthening a paper or abstract by
making specific suggestions. For instance, AI algorithms can suggest
previous relevant studies to include in the introduction or limita-
tions to include in the discussion sections of a manuscript. However,
current limitations of AI algorithms include limited access to all pub-
lications and the inability to identify the most recent studies. Fur-
thermore, the AI tool might propose an appropriate study sample
size based on previous studies and computed power calculations or
suggest the most appropriate statistical test to use based on the study
distribution1.
The integration of AI into academic writing streamlines the crea-
tive and writing process, increasing productivity and content. The
research process can present challenges, particularly for trainees or
young investigators with limited experience. Frequently, the most diffi-
cult stage of a research project is generating a hypothesis and initiating
a study. Groups with limited experience could benefit tremendously
from AI, as it might stimulate a creative process in a particular field of
interest and/or identify gaps in the literature2. We demonstrated this
application through an experiment in which we used ChatGPT (Box 1)
to generate project ideas in the field of male reproductive health
3
. The
model currently only has access to publications up to 2021 and might
not have comprehensive access to previous publication literature, but
the AI algorithm was able to propose novel and relevant topics that we
have explored over the past 12 months4–6.
The use of AI in academia has proved to be a valuable tool in stream-
lining the research process with data processing applications. By pro-
cessing large amounts of data, AI algorithms can identify important
findings, potentially saving researchers hours of manual data analysis7.
In addition, AI applications have enabled the rapid identification of
previously elusive or hard-to-detect insights and trends, which would
have been time-consuming or even impossible to uncover using previ-
ously existing software or other, manual methods
8
. Current examples
include choosing the most appropriate sperm in fertility clinics
9
or
predicting lymph node metastasis through analysis of primary pros-
tate tumour tissue
10
. Furthermore, tools such as statistics calculators,
Microsoft Excel and BioRender are used every day to aid in organizing
and presenting data. Similarly, AI algorithms can rapidly produce well-
organized and aesthetic data outputs such as figures and tables that
can be used in manuscripts and presentations. The use of AI-generated
content in academic writing also serves to streamline the editing
process and minimize time spent on tasks such as grammatical and
structural editing. The application of AI algorithms in the creation of
various sections of an academic manuscript such as the introduction,
discussion and conclusion is a logical extension of this process. These
AI-generated sections might not be fully comprehensive or directly
applicable to the study at hand, but they could serve as an initial guide
that can be edited. Before submitting this commentary, ChatGPT
effectively identified grammatical errors, instances of passive voice
and provided recommendations, including the inclusion of additional
examples and refinement of the structural order.
We are witnessing a powerful integration of AI into several aspects
of daily life. Many liken the recent advances in AI-based tools to the
development of the internet in the 1990s. The internet revolutionized
our ability to readily access and efficiently process large amounts of
data. However, as with all technological advances, initial hesitation
exists for adoption as well as concern for potential risks or downsides.
Indeed, some have legitimate concerns about widespread adoption of
AI-based tools. For example, reliance on AI-based outputs could act
to curb human innovation and critical thinking skills. Furthermore,
Check for updates
nature reviews urology
4. Khullar, D. Social media and medical misinformation: confronting new variants of an old
problem. JAMA 328, 1393–1394 (2022).
5. Reddy, R. V. et al. Assessing the quality and readability of online content on shock wave
therapy for erectile dysfunction. Andrologia 54, e14607 (2022).
6. Khodamoradi, K., Golan, R., Dullea, A. & Ramasamy, R. Exosomes as potential biomarkers
for erectile dysfunction, varicocele, and testicular injury. Sex. Med. Rev. 10, 311–322 (2022).
7. Stone, L. You’ve got a friend online. Nat. Rev. Urol. 17, 320 (2020).
8. Pai, R. K. et al. A review of current advancements and limitations of artiicial intelligence in
genitourinary cancers. Am. J. Clin. Exp. Urol. 8, 152–162 (2020).
9. You, J. B. et al. Machine learning for sperm selection. Nat. Rev. Urol. 18, 387–403 (2021).
10. Stone, L. The dawning of the age of artiicial intelligence in urology. Nat. Rev. Urol. 18, 322
(2021).
Acknowledgements
The manuscript was edited for grammar and structure using the advanced language model
ChatGPT. The authors thank S. Verma for addressing inquiries related to artiicial intelligence.
Competing interests
R.R. is funded by the National Institutes of Health Grant R01 DK130991 and the Clinician
Scientist Development Grant from the American Cancer Society. The other authors declare
no competing interests.
Related links
ChatGPT: https://chat.openai.com/
Cohere: https://cohere.ai/
CoSchedule Headline Analyzer: https://coschedule.com/headline-analyzer
DALL-E 2: https://openai.com/dall-e-2/
Elicit: https://elicit.org/
Penelope.ai: https://www.penelope.ai/
Quillbot: https://quillbot.com/
Semantic Scholar: https://www.semanticscholar.org/
Wordtune by AI21 Labs: https://www.wordtune.com/
Writefull: https://www.writefull.com/
AI outputs are a by-product of inputs, and inputs have the potential to
be error prone or biased.
We do not suggest that AI could be a replacement for human inno-
vative thinking or a reliance on AI in the absence of critical reflection.
However, we argue that integration of AI into daily life is inevitable,
and resourceful individuals should understand this technology and
take advantage of its usefulness. For the academic, this use entails
applications for sparking creative project ideas, optimizing study
design, organizing data and data analysis, and synthesizing academic
writing.
Roei Golan   1,3, Rohit Reddy2,3, Akhil Muthigi2 &
Ranjith Ramasamy2
1Department of Clinical Sciences, Florida State University College
of Medicine, Tallahassee, FL, USA. 2Desai Sethi Urology Institute,
University of Miami Miller School of Medicine, Miami, FL, USA.
3These authors contributed equally: Roei Golan, Rohit Reddy.
e-mail: ramasamy@miami.edu
Published online: xx xx xxxx
References
1. Checco, A., Bracciale, L., Loreti, P., Pinield, S. & Bianchi, G. AI-assisted peer review.
Humanit. Soc. Sci. Commun. 8, 25 (2021).
2. Hutson, M. Could AI help you to write your next paper? Nature 611, 192–193 (2022).
3. Krzastek, S. C., Farhi, J., Gray, M. & Smith, R. P. Impact of environmental toxin exposure
on male fertility potential. Transl Androl. Urol. 9, 2797–2813 (2020).
Box 1
Current articial intelligence tools that can be used in academia
For literature reviewa
Semantic Scholar provides access to scientiic literature in practically
every academic ield. Researchers can eiciently locate relevant
papers and studies to support their own research or writing. Writers
can also use this tool to discover new papers and authors and
institutions that are working on related topics.
Penelope.ai analyses and understands large sets of text, such
as scientiic papers or research articles, to help writers identify key
themes, concepts and trends in the literature.
Elicit helps scientiic writers ind published manuscripts that might
not be regularly indexed by existing databases, aiding discovery of
new and emerging research that can support their own writing.
For writingb
Writefull improves grammar, style and readability of inputted writing.
It can help researchers submit more polished and professional
writing.
CoSchedule Headline Analyzer is a tool that speciically helps
with manuscript title creation. It can analyse inputted headlines and
suggest modiications based on word balance, length and structure.
CoSchedule helps writers create headlines that are more engaging
and eective.
Quillbot is a tool that uses machine learning algorithms to reduce
syntax complexity and increase clarity.
Wordtune uses automated feedback on grammar, style and
readability.
ChatGPT is an OpenAI tool that has a chatbot–user interface that
can be used to clarify, ine-tune and polish excerpts of writing. It can
also be used to plan study design and statistical approaches.
Combined literature review and writingb
Cohere can be used by researchers and scientiic authors to generate
summaries, outlines and entire manuscript sections based on a given
set of sources.
Figures
DALL-E 2 is an OpenAI tool that can be used to generate images
from text descriptions. This tool can be useful for creating visual
aids to support the writing, making it more engaging and easier to
understand. Currently, this tool is primarily used to generate creative
illustrations.
aPitfalls of artiicial intelligence (AI)-powered literature review tools are their limited access to indexed databases, potentially inaccurate algorithms being used
to understand bulk text, and the cost of these tools making them prohibitive to some researchers. bPitfalls of AI-assisted academic writing are a potential lack
of human-authored nuance in the writing, reduced originality and creativity, and few people have an understanding of the interfaces or can aord the tools.
Anecdotally, when asked to do a systematic review on the eect of vasectomy on lower urinary tract symptoms, the AI tool ChatGPT generated ten studies and
a relative risk when only two studies evaluated the potential association.
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Full-text available
  • Dec 2020
Idiopathic infertility is the most common individual diagnosis in male infertility, representing nearly 44% of cases. Research studies dating over the last half-century consistently demonstrate a decline in male fertility that is incompletely explained by obesity, known genetic causes, or diet and lifestyle changes alone. Human exposures have changed dramatically over the same time course as this fertility decline. Synthetic chemicals surround us. Some are benevolent; however, many are known to cause disruption of the hypothalamic-pituitary-gonadal axis and impair spermatogenesis. More than 80,000 chemicals are registered with the United States National Toxicology Program and nearly 2,000 new chemicals are introduced each year. Many of these are known toxins, such as phthalates, polycyclic aromatic hydrocarbons, aromatic amines, and organophosphate esters, and have been banned or significantly restricted by other countries as they carry known carcinogenic effects and are reproductively toxic. In the United States, many of these chemicals are still permissible in exposure levels known to cause reproductive harm. This contrasts to other chemical regulatory legislature, such as the European Union's REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) regulations which are more comprehensive and restrictive. Quantification of these diverse exposures on an individual level has proven challenging, although forthcoming technologies may soon make this data available to consumers. Establishing causality and the proportion of idiopathic infertility attributable to environmental toxin exposures remains elusive, however, continued investigation, avoidance of exposure, and mitigation of risk is essential to our reproductive health. The aim of this review is to examine the literature linking changes in male fertility to some of the most common environmental exposures. Specifically, pesticides and herbicides such as dichlorodiphenyltrichloroethane (DDT), dibromochloropropane (DBCP), organophosphates and atrazine, endocrine disrupting compounds including plastic compounds phthalates and bisphenol A (BPA), heavy metals, natural gas/oil, non-ionizing radiation, air and noise pollution, lifestyle factors including diet, obesity, caffeine use, smoking, alcohol and drug use, as well as commonly prescribed medications will be discussed.
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Social Media and Medical Misinformation: Confronting New Variants of an Old Problem
Article
  • Sep 2022
This Viewpoint describes several proposals to mitigate the role of social media in medical misinformation from the ABIM Foundation’s 2022 Forum, including algorithmic adjustment, misinformation research and surveillance, and medical professional training and community engagement.
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Machine learning for sperm selection
Article
  • May 2021
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection — selecting the most promising candidate from 10⁸ gametes — presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.
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A review of current advancements and limitations of artificial intelligence in genitourinary cancers
Article
  • Oct 2020
Advances in deep learning and neural networking have allowed clinicians to understand the impact that artificial intelligence (AI) could have on improving clinical outcomes and resources expenditures. In the realm of genitourinary (GU) cancers, AI has had particular success in improving the diagnosis and treatment of prostate, renal, and bladder cancers. Numerous studies have developed methods to utilize neural networks to automate prognosis prediction, treatment plan optimization, and patient education. Furthermore, many groups have explored other techniques, including digital pathology and expert 3D modeling systems. Compared to established methods, nearly all the studies showed some level of improvement and there is evidence that AI pipelines can reduce the subjectivity in the diagnosis and management of GU malignancies. However, despite the many potential benefits of utilizing AI in urologic oncology, there are some notable limitations of AI when combating real-world data sets. Thus, it is vital that more prospective studies be conducted that will allow for a better understanding of the benefits of AI to both cancer patients and urologists.
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