Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses
Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening,...
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| Published in | Journal of medical Internet research Vol. 26; no. 11; p. e56780 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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Journal of Medical Internet Research
25.06.2024
JMIR Publications |
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| Abstract | Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses. |
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| AbstractList | Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses. Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses.Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses. Large language models (LLMs) like ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be employed to expedite various steps, including defining clinical questions, literature search, document screening, information extraction, and language refinement, etc, thereby conserving resources and enhancing efficiency. However, when utilizing LLMs, attention should be given to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. This article reviews the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors involved in systematic reviews and meta-analyses. |
| Audience | Academic |
| Author | Luo, Xufei Wang, Ye Chen, Yaolong Chen, Fengxian Wang, Qi Lyu, Meng Liu, Hui Wang, Zijun Zhu, Di Wang, Ling |
| AuthorAffiliation | 1 Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou China 7 School of Public Health Lanzhou University Lanzhou China 8 Department of Health Research Methods, Evidence and Impact Faculty of Health Sciences McMaster University Hamilton, ON Canada 4 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou University Lanzhou China 5 Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017) School of Basic Medical Sciences Lanzhou University Lanzhou China 9 McMaster Health Forum McMaster University Hamilton, ON Canada 2 World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation Lanzhou China 3 Institute of Health Data Science Lanzhou University Lanzhou China 6 School of Information Science & Engineering Lanzhou University Lanzhou China |
| AuthorAffiliation_xml | – name: 7 School of Public Health Lanzhou University Lanzhou China – name: 5 Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017) School of Basic Medical Sciences Lanzhou University Lanzhou China – name: 8 Department of Health Research Methods, Evidence and Impact Faculty of Health Sciences McMaster University Hamilton, ON Canada – name: 6 School of Information Science & Engineering Lanzhou University Lanzhou China – name: 1 Evidence-Based Medicine Center School of Basic Medical Sciences Lanzhou University Lanzhou China – name: 2 World Health Organization Collaboration Center for Guideline Implementation and Knowledge Translation Lanzhou China – name: 9 McMaster Health Forum McMaster University Hamilton, ON Canada – name: 3 Institute of Health Data Science Lanzhou University Lanzhou China – name: 4 Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province Lanzhou University Lanzhou China |
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| Copyright | COPYRIGHT 2024 Journal of Medical Internet Research Xufei Luo, Fengxian Chen, Di Zhu, Ling Wang, Zijun Wang, Hui Liu, Meng Lyu, Ye Wang, Qi Wang, Yaolong Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.06.2024. Xufei Luo, Fengxian Chen, Di Zhu, Ling Wang, Zijun Wang, Hui Liu, Meng Lyu, Ye Wang, Qi Wang, Yaolong Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.06.2024. 2024 |
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| Title | Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses |
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