Exploring the frontiers of artificial intelligence: A bibliometric analysis of high-impact research up to 2023

Putri Hana Pebriana, Edi Setiadi, Dadi Ahmadi, Robbi Rahim, Ok Dedy Arwansyah, Kusuma Wijayanto, Rholand Muary, Elkana Timotius, Dede Aji Mardani, Aja Rowikarim, Irwan Fauzy Ridwan

Article ID: 10176
Vol 9, Issue 2, 2025


Abstract


Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.


Keywords


AI; bibliometric analysis; citation metric; development

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References


Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

Ahmad, I., Ahmed, G., Shah, S. A. A., & Ahmed, E. (2020). A decade of big data literature: analysis of trends in light of bibliometrics. The Journal of Supercomputing, 76(5), 3555–3571. https://doi.org/10.1007/s11227-018-2714-x

Ahmad, S. T., Watrianthos, R., Samala, A. D., Muskhir, M., & Dogara, G. (2023). Project-based Learning in Vocational Education: A Bibliometric Approach. International Journal Modern Education and Computer Science, 15(4), 43–56. https://doi.org/10.5815/ijmecs.2023.04.04

Albu, A., Enescu, A., & Malagò, L. (2020). Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder. Proceedings of the Northern Lights Deep Learning Workshop, 1, 6. https://doi.org/10.7557/18.5172

Antonov, A., & Kerikmäe, T. (2020). Trustworthy AI as a Future Driver for Competitiveness and Social Change in the EU. In The EU in the 21st Century (pp. 135–154). Springer International Publishing. https://doi.org/10.1007/978-3-030-38399-2_9

Arees, Z. A. (2022). The Social Impact of Artificial Intelligence. In Encyclopedia of Data Science and Machine Learning (pp. 834–847). IGI Global. https://doi.org/10.4018/978-1-7998-9220-5.ch048

Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Borgohain, D. J., Bhardwaj, R. K., & Verma, M. K. (2022). Mapping the literature on the application of artificial intelligence in libraries (AAIL): a scientometric analysis. Library Hi Tech. https://doi.org/10.1108/LHT-07-2022-0331

Burnham, J. F. (2006). Scopus database: A review. In Biomedical Digital Libraries (Vol. 3). https://doi.org/10.1186/1742-5581-3-1

Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). nuScenes: A Multimodal Dataset for Autonomous Driving. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11618–11628. https://doi.org/10.1109/CVPR42600.2020.01164

Choi, J., Shin, K., Jung, J., Bae, H.-J., Kim, D. H., Byeon, J.-S., & Kim, N. (2020). Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for Endoscopy. Clinical Endoscopy, 53(2), 117–126. https://doi.org/10.5946/ce.2020.054

Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021). Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges. IEEE Transactions on Intelligent Transportation Systems, 22(3), 1341–1360. https://doi.org/10.1109/TITS.2020.2972974

Gao, A. (2022). National Strategy for the development of Artificial Intelligence In the context of the global digital economy. Artificial Societies, 17(2), 0. https://doi.org/10.18254/S207751800020634-6

Gao, H., Cheng, B., Wang, J., Li, K., Zhao, J., & Li, D. (2018). Object Classification Using CNN-Based Fusion of Vision and LIDAR in Autonomous Vehicle Environment. IEEE Transactions on Industrial Informatics, 14(9), 4224–4231. https://doi.org/10.1109/TII.2018.2822828

Gorraiz, J. I. (2021). Editorial: Best Practices in Bibliometrics & Bibliometric Services. Frontiers in Research Metrics and Analytics, 6. https://doi.org/10.3389/frma.2021.771999

Kim, J., & Park, N. (2020). Blockchain-Based Data-Preserving AI Learning Environment Model for AI Cybersecurity Systems in IoT Service Environments. Applied Sciences, 10(14), 4718. https://doi.org/10.3390/app10144718

Matheny, M., Thadaney, I. S., Ahmed, M., Whicher, D. (2019). Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. National Academy of Medicine.

Mshvidobadze, T. (2021). Python for Automating Machine Learning Tasks. JINAV: Journal of Information and Visualization, 2(2), 77–82. https://doi.org/10.35877/454RI.jinav373

Mustapa, M. (2023). Implementation of Feature Selection and Data Split using Brute Force to Improve Accuracy. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 14(1), 50–59. https://doi.org/10.58346/JOWUA.2023.I1.004

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. British Journal of Ophthalmology, 106(7), 889–892. https://doi.org/10.1136/bjophthalmol-2022-321141

Negahdary, M., Jafarzadeh, M., Rahimi, G., Naziri, M., & Negahdary, A. (2018). The Modified h-Index of Scopus: A New Way in Fair Scientometrics. Publishing Research Quarterly, 34(3). https://doi.org/10.1007/s12109-018-9587-y

Ninkov, A., Frank, J. R., & Maggio, L. A. (2022). Bibliometrics: Methods for studying academic publishing. Perspectives on Medical Education, 11(3). https://doi.org/10.1007/s40037-021-00695-4

Ronal Watrianthos, & Yuhefizar, Y. (2023). Exploring Research Trends and Impact: A Bibliometric Analysis of RESTI Journal from 2018 to 2022. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(4), 970–981. https://doi.org/10.29207/resti.v7i4.5101

Sabeel, U., Heydari, S. S., Elgazzar, K., & El-Khatib, K. (2021). Building an Intrusion Detection System to Detect Atypical Cyberattack Flows. IEEE Access, 9, 94352–94370. https://doi.org/10.1109/ACCESS.2021.3093830

Samala, A. D., Bojic, L., Bekiroğlu, D., Watrianthos, R., & Hendriyani, Y. (2023). Microlearning: Transforming Education with Bite-Sized Learning on the Go—Insights and Applications. International Journal of Interactive Mobile Technologies (IJIM), 17(21), 4–24. https://doi.org/10.3991/ijim.v17i21.42951

Sarvamangala, D. R., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: a survey. Evolutionary Intelligence, 15(1), 1–22. https://doi.org/10.1007/s12065-020-00540-3

Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M., & Liu, W. (2020). Vision to Language: Methods, Metrics and Datasets (pp. 9–62). https://doi.org/10.1007/978-3-030-49724-8_2

Sundaresan, N. (2019). From Code to Data. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 3175–3175. https://doi.org/10.1145/3292500.3340410

Supriadi, M., Jondri, J., and, I. I.-J. J. of I., & 2023, undefined. (2023). Retweet Prediction Using Artificial Neural Network Method Optimized with Firefly Algorithm. Sainsmat.OrgMR Supriadi, J Jondri, I IndwiartiJINAV: Journal of Information and Visualization, 2023 sainsmat. Org, 4(2), 2746–1440. https://sainsmat.org/index.php/jinav/article/view/1903

Tran, B. X., McIntyre, R. S., Latkin, C. A., Phan, H. T., Vu, G. T., Nguyen, H. L. T., Gwee, K. K., Ho, C. S. H., & Ho, R. C. M. (2019). The Current Research Landscape on the Artificial Intelligence Application in the Management of Depressive Disorders: A Bibliometric Analysis. International Journal of Environmental Research and Public Health, 16(12), 2150. https://doi.org/10.3390/ijerph16122150

Watrianthos, R., Triono Ahmad, S., & Muskhir, M. (2023). Charting the Growth and Structure of Early ChatGPT-Education Research: A Bibliometric Study. Journal of Information Technology Education: Innovations in Practice, 22, 235–253. https://doi.org/10.28945/5221

Whittlestone, J., & Clarke, S. (2022). AI Challenges for Society and Ethics. In The Oxford Handbook of AI Governance. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780197579329.013.3

Wijethunga, R. L. M. A. P. C., Matheesha, D. M. K., Noman, A. Al, De Silva, K. H. V. T. A., Tissera, M., & Rupasinghe, L. (2020). Deepfake Audio Detection: A Deep Learning Based Solution for Group Conversations. 2020 2nd International Conference on Advancements in Computing (ICAC), 192–197. https://doi.org/10.1109/ICAC51239.2020.9357161

Windarto, A. P., Wanto, A., Solikhun, S., & Watrianthos, R. (2023). A Comprehensive Bibliometric Analysis of Deep Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(5), 1155–1164. https://doi.org/10.29207/resti.v7i5.5274




DOI: https://doi.org/10.24294/jipd10176

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