Global insights and the impact of generative AI-ChatGPT on multidisciplinary: a systematic review and bibliometric analysis

IF 3.2 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Connection Science Pub Date : 2024-05-16 DOI:10.1080/09540091.2024.2353630
Nauman Khan, Zahid Khan, Anis Koubaa, Muhammad Khurram Khan, Rosli bin Salleh
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Abstract

In 2022, OpenAI’s unveiling of generative AI Large Language Models (LLMs)- ChatGPT, heralded a significant leap forward in human-machine interaction through cutting-edge AI technologies. With its surging popularity, scholars across various fields have begun to delve into the myriad applications of ChatGPT. While existing literature reviews on LLMs like ChatGPT are available, there is a notable absence of systematic literature reviews (SLRs) and bibliometric analyses assessing the research’s multidisciplinary and geographical breadth. This study aims to bridge this gap by synthesizing and evaluating how ChatGPT has been integrated into diverse research areas, focusing on its scope and the geographical distribution of studies. Through a systematic review of scholarly articles, we chart the global utilization of ChatGPT across various scientific domains, exploring its contribution to advancing research paradigms and its adoption trends among di ff erent disciplines. Our findings reveal a widespread endorsement of ChatGPT across multiple fields, with significant implementations in healthcare (38.6%), computer science / IT (18.6%), and education / research (17.3%). Moreover, our demographic analysis underscores ChatGPT’s global reach and accessibility, indicating participation from 80 unique countries in ChatGPT-related research, with the most frequent countries keyword occurrence, USA (719), China (181), and India (157) leading in contributions. Additionally, our study highlights the leading roles of institutions such as King Saud University, the All India Institute of Medical Sciences, and Taipei Medical University in pioneering ChatGPT research in our dataset. This research not only sheds light on the vast opportunities and challenges posed by ChatGPT in scholarly pursuits but also acts as a pivotal resource for future inquiries. It emphasizes that the generative AI (LLM) role is revolutionizing every field. The insights provided in this paper are particularly valuable for academics, researchers, and practitioners across various disciplines, as well as policymakers looking to grasp the extensive reach and impact of generative AI technologies like ChatGPT in the global research community.
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生成式人工智能--ChatGPT的全球洞察力及其对多学科的影响:系统回顾与文献计量分析
2022 年,OpenAI 推出了生成式人工智能大型语言模型(LLMs)--ChatGPT,预示着尖端人工智能技术在人机交互领域的重大飞跃。随着 ChatGPT 的迅速普及,各领域的学者们开始深入研究 ChatGPT 的各种应用。虽然已有关于 ChatGPT 等 LLM 的文献综述,但明显缺乏系统性文献综述(SLR)和文献计量分析来评估研究的多学科性和地域广泛性。本研究旨在弥合这一差距,综合评估 ChatGPT 如何融入不同的研究领域,重点关注其研究范围和地理分布。通过对学术文章的系统回顾,我们描绘了 ChatGPT 在各个科学领域的全球使用情况,探讨了它对推进研究范式的贡献及其在不同学科中的应用趋势。我们的研究结果表明,ChatGPT 在多个领域得到了广泛认可,在医疗保健(38.6%)、计算机科学/信息技术(18.6%)和教育/研究(17.3%)领域得到了大量应用。此外,我们的人口分析强调了 ChatGPT 的全球影响力和可访问性,表明有 80 个国家参与了与 ChatGPT 相关的研究,其中出现关键词最多的国家是美国(719)、中国(181)和印度(157)。此外,我们的研究还强调了沙特国王大学、全印度医学科学研究所和台北医学大学等机构在数据集中的 ChatGPT 研究中发挥的主导作用。这项研究不仅揭示了 ChatGPT 在学术研究中带来的巨大机遇和挑战,还为未来的研究提供了重要资源。它强调了生成式人工智能(LLM)的作用正在彻底改变各个领域。本文所提供的见解对于各学科的学者、研究人员和从业人员,以及希望掌握像 ChatGPT 这样的生成式人工智能技术在全球研究界的广泛覆盖范围和影响的政策制定者来说尤为宝贵。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Connection Science
Connection Science 工程技术-计算机:理论方法
CiteScore
6.50
自引率
39.60%
发文量
94
审稿时长
3 months
期刊介绍: Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.
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