ChatGPT and other artificial intelligence applications speed up scientific writing

Chen, Tzeng Ji

doi:10.1097/jcma.0000000000000900

Cited by 148 publications

(97 citation statements)

References 16 publications

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“…Additionally, ChatGPT can be helpful in generating queries for comprehensive systematic review with high precision, as shown by Wang et al, despite the authors highlighting the transparency issues and unsuitability for high-recall retrieval [ 61 ]. Moreover, the utility of ChatGPT extends to involve an improvement in language and a better ability to express and communicate research ideas and results, ultimately speeding up the publication process with the faster availability of research results [ 23 , 25 , 29 , 40 , 48 , 63 , 66 ]. This is particularly relevant for researchers who are non-native English speakers [ 23 , 25 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the utility of ChatGPT extends to involve an improvement in language and a better ability to express and communicate research ideas and results, ultimately speeding up the publication process with the faster availability of research results [ 23 , 25 , 29 , 40 , 48 , 63 , 66 ]. This is particularly relevant for researchers who are non-native English speakers [ 23 , 25 , 63 ]. Such a practice can be acceptable considering the already existent English editing services provided by several academic publishers.…”

Section: Discussionmentioning

confidence: 99%

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ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

2023

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ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

2023

View full text Add to dashboard Cite

show abstract

“…Additionally, ChatGPT can be helpful in generating queries for comprehensive systematic review with high precision as shown by Wang et al despite the authors highlighting the transparency issues and unsuitability for high-recall retrieval [23]. Moreover, the utility of ChatGPT extends to involve the improvement in language and better ability to express and communicate the research ideas and results, ultimately speeding up the publication process with faster availability of research results [26,29,35,36,44,49,77]. This is particularly relevant for researchers who are non-native English speakers [26,29,35].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the utility of ChatGPT extends to involve the improvement in language and better ability to express and communicate the research ideas and results, ultimately speeding up the publication process with faster availability of research results [26,29,35,36,44,49,77]. This is particularly relevant for researchers who are non-native English speakers [26,29,35]. Such a practice can be acceptable considering the already existent English editing services provided by several academic publishers as an acceptable practice.…”

Section: Discussionmentioning

confidence: 99%

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The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations

Sallam¹

2023

Preprint

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An artificial intelligence (AI)-based conversational large language model (LLM) was launched in November 2022 namely, “ChatGPT”. Despite the wide array of potential applications of LLMs in healthcare education, research and practice, several valid concerns were raised. The current systematic review aimed to investigate the possible utility of ChatGPT and to highlight its limitations in healthcare education, research and practice. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar under the term “ChatGPT”. Eligibility criteria included the published research or preprints of any type that discussed ChatGPT in the context of healthcare education, research and practice. A total of 280 records were identified, and following full screening, a total of 60 records were eligible for inclusion. Benefits/applications of ChatGPT were cited in 51/60 (85.0%) records with the most common being the utility in scientific writing followed by benefits in healthcare research (efficient analysis of massive datasets, code generation and rapid concise literature reviews besides utility in drug discovery and development). Benefits in healthcare practice included cost saving, documentation, personalized medicine and improved health literacy. Concerns/possible risks of ChatGPT use were expressed in 58/60 (96.7%) records with the most common being the ethical issues including the risk of bias, plagiarism, copyright issues, transparency issues, legal issues, lack of originality, incorrect responses, limited knowledge, and inaccurate citations. Despite the promising applications of ChatGPT which can result in paradigm shifts in healthcare education, research and practice, the embrace of this application should be done with extreme caution. Specific applications of ChatGPT in health education include the promising utility in personalized learning tools and shift towards more focus on critical thinking and problem-based learning. In healthcare practice, ChatGPT can be valuable for streamlining the workflow and refining personalized medicine. Saving time for the focus on experimental design and enhancing research equity and versatility are the benefits in scientific research. Regarding authorship in scientific articles, as it currently stands, ChatGPT does not qualify to be listed as an author unless the ICJME/COPE guidelines are revised and amended. An initiative involving all stakeholders involved in healthcare education, research and practice is urgently needed to set a code of ethics and conduct on the responsible practices involving ChatGPT among other LLMs.

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It takes one to know one—Machine learning for identifying OBGYN abstracts written by ChatGPT

Levin,

Meyer,

Guigue

et al. 2024

Intl J Gynecology & Obste

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ObjectivesTo use machine learning to optimize the detection of obstetrics and gynecology (OBGYN) Chat Generative Pre‐trained Transformer (ChatGPT) ‐written abstracts of all OBGYN journals.MethodsWe used Web of Science to identify all original articles published in all OBGYN journals in 2022. Seventy‐five original articles were randomly selected. For each, we prompted ChatGPT to write an abstract based on the title and results of the original abstracts. Each abstract was tested by Grammarly software and reports were inserted into a database. Machine‐learning modes were trained and examined on the database created.ResultsOverall, 75 abstracts from 12 different OBGYN journals were randomly selected. There were seven (58%) Q1 journals, one (8%) Q2 journal, two (17%) Q3 journals, and two (17%) Q4 journals. Use of mixed dialects of English, absence of comma‐misuse, absence of incorrect verb forms, and improper formatting were important prediction variables of ChatGPT‐written abstracts. The deep‐learning model had the highest predictive performance of all examined models. This model achieved the following performance: accuracy 0.90, precision 0.92, recall 0.85, area under the curve 0.95.ConclusionsMachine‐learning‐based tools reach high accuracy in identifying ChatGPT‐written OBGYN abstracts.

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ChatGPT and other artificial intelligence applications speed up scientific writing

Cited by 148 publications

References 16 publications

ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns

The Utility of ChatGPT as an Example of Large Language Models in Healthcare Education, Research and Practice: Systematic Review on the Future Perspectives and Potential Limitations

It takes one to know one—Machine learning for identifying OBGYN abstracts written by ChatGPT

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