Analisis Bibliometrik Global Pemanfaatan Artificial Intelligence Dalam Penelitian Resistensi Antimikroba Periode 2015–2025
Keywords:
Artificial Intelligence, Antimicrobial Resistance, Bibliometric, Antimicrobial Stewardship
Abstract
Antimicrobial resistance (AMR) is a growing global health threat. Artificial Intelligence (AI) offers innovative approaches to the detection, prediction, and management of resistance through advanced data analysis. This study aims to map the research landscape of AI utilisation in AMR for the period 2015–2025 through bibliometric analysis. A search on PubMed (4 September 2025) yielded 2,078 documents (1996–2025), and after applying year restrictions, 2,009 articles were analysed. The total output of countries reached 11,943 publications, with China (5,570), the United States (2,896), and India (755) as the main contributors. Four thematic clusters were identified: machine learning, bacterial resistance, deep learning, and antimicrobial stewardship. The results showed a sharp increase in publications until 2025, with the journals Antibiotics, Frontiers in Microbiology, and Scientific Reports dominating. The study confirmed the important role of AI in supporting resistance detection and precision medicine, but challenges such as data limitations, algorithmic bias, and global research inequality still require attention.
References
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Mostafaei, A., Akbari, H., Lundborg, C. S., Kabiri, N., Vahed, N., Hajebrahimi, S., & Doshmangir, L. (2023). A systematic synthesis of expert opinion on effective policies to tackle bacterial resistance worldwide. Veterinary Medicine and Science, 9(3), 1395–1406. https://doi.org/10.1002/vms3.1127
Payumo, J., Alocilja, E., Boodoo, C., LuchiniColbry, K., Ruegg, P., McLamore, E., Vanegas, D., Briceno, R., CastanedaSabogal, A., Watanabe, K., Gordoncillo, M., Amalin, D., Fernando, L., & Bhusal, N. (2021). Next Generation of AMR Network. Encyclopedia, 1(3), 871–892. https://doi.org/10.3390/encyclopedia10300 67
Rahit, K. M. T. H., Avramovic, V., Chong, J. X., & Tarailo-Graovac, M. (2024). GPAD: A natural language processing-based application to extract the gene-disease association discovery information from OMIM. BMC Bioinformatics, 25(1), 84. https://doi.org/10.1186/s12859-024- 05693-x
Reperant, L., Mackenzie, J., Venter, M., Mulumba, M., & Osterhaus, A. (2025). Scientific highlights of the 8th world one health Congress, Cape Town, South Africa 2024. One Health Outlook, 7(1), 20, s42522-025- 00143–x. https://doi.org/10.1186/s42522- 025-00143-x
Sakagianni, A., Koufopoulou, C., Feretzakis, G., Kalles, D., Verykios, V. S., Myrianthefs, P., & Fildisis, G. (2023). Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review. Antibiotics, 12(3), 452.https://doi.org/10.33 90/antibiotics12030452
Tang, K. W. K., Millar, B. C., & Moore, J. E. (2023). Antimicrobial Resistance (AMR). British Journal of Biomedical Science, 80, 11387. https://doi.org/10.3389/bjbs.2023.11387
Tang, S., Xu, Y., & Li, X. (2024). Worldwide trend in research on Candida albicans and cancer correlations: A comprehensive bibliometric analysis. Frontiers in Microbiology, 15, 1398527.https://doi.org/10.3389/fmicb.2024.1398527
Wang, Z., Zhu, G., & Li, S. (2025a). Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: Bibliometric and visualization analysis. Frontiers in Medicine,12,1492709.https://doi.org/10.3389/fmed.2025.1492709
Wang, Z., Zhu, G., & Li, S. (2025b). Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: Bibliometric and visualization analysis. Frontiers in Medicine, 12, 1492709.https://doi.org/10.3389/fmed.2025.1492709
Yang, J., Eyre, D. W., & Clifton, D. A. (2023). Interpretable Machine Learning-based Decision Support for Prediction of Antibiotic Resistance for Complicated Urinary Tract Infections. Health Informatics.https://doi.org/10.1101/2023.01.09.23284
Alotaibi, A., Contreras, R., Thakker, N., Mahapatro, A., Adla Jala, S. R., Mohanty, E., Devulapally, P., Mirchandani, M., Marsool Marsool, M. D., Jain, S. M., Joukar, F., Alizadehasl, A., Hosseini Jebelli, S. F., Amini-Salehi, E., & Ameen, D. (2025). Bibliometric analysis of artificial intelligence Gambar 4. Perkembangan publikasi jurnal utama applications in cardiovascular imaging: Trends, impact, and emerging research areas. Annals of Medicine & Surgery, 87(4),19471968. https://doi.org/10.1097/M S9.0000000000003080
Antonie, N. I., Gheorghe, G., Ionescu, V. A., Tiucă, L.-C., & Diaconu, C. C. (2025). The Role of ChatGPT and AI Chatbots in Opti mizing Antibiotic Therapy: A Comprehensi ve Narrative Review. Antibiotics, 14(1), 60. https://doi.org/10.3390/antibiotics1401006 0
Aoujil, Z., Hanine, M., Flores, E. S., Samad, Md. A., & Ashraf, I. (2023). Artificial Intelligence and Behavioral Economics: A Bibliographic Analysis of Research Field. IEEE Access, 11,139367– 139394. https://doi.org/10.110 9/ACCESS.2023.3339778
Apriantoro, M. S., & Maulana, I. (2025). Environmental Taxation Through a Bibliometric Lens: Finding Leading Voices, Trends, and Gaps. International Journal of Energy Economics and Policy, 15(2), 484– 494. https://doi.org/10.32479/ijeep.17815
Aslam, B., Asghar, R., Muzammil, S., Shafique, M., Siddique, A. B., Khurshid, M., Ijaz, M., Rasool, M. H., Chaudhry, T. H., Aamir, A., & Baloch, Z. (2024). AMR and Sustainable Development Goals: At a crossroads. Globalization and Health, 20(1), 73. https://doi.org/10.1186/s12992-024- 01046-8
Branda, F., & Scarpa, F. (2024). Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future. Antibiotics, 13(6), 502. https://doi. org/10.3390/antibiotics13060502
Branda, F., & Scarpa, F. (2024b). Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future. Antibiotics, 13(6),502. https://doi.org/10.3390/antibiotics13060502
Chua, A. Q., Verma, M., Hsu, L. Y., & LegidoQuigley, H. (2021). An analysis of national action plans on antimicrobial resistance in Southeast Asia using a governance framework approach. The Lancet Regional Health - Western Pacific, 7, 100084. https://doi.org/10.1016/j.lanwpc.2020.1000 84
De La Lastra, J. M. P., Wardell, S. J. T., Pal, T., De La Fuente-Nunez, C., & Pletzer, D. (2024). From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review. Journal of Medical Systems, 48(1), 71. https://doi.or g/10.1007/s10916-024-02089-5
Dong, Y., Quan, H., Ma, C., Shan, L., & Deng, L. (2024). TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning. International Journal of Molecular Sciences, 25(13), 7228. https://doi.org/10. 3390/ijms25137228
Elalouf, A., Elalouf, H., Rosenfeld, A., & Maoz, H. (2025). Artificial intelligence in drug resistance management. 3 Biotech, 15(5), 126. https://doi.org/10.1007/s13205-025- 04282-w
Facchiano, A., Angelini, C., Bosotti, R., Guffanti, A., Marabotti, A., Marangoni, R., Pascarella, S., Romano, P., Zanzoni, A., & Helmer-Citterich, M. (2015). Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics. BMC Bioinformatics, 16(S9), S1, 1471-2105-16- S9–S1. https://doi.org/10.1186/1471-2105- 16-S9-S1
Fanelli, U., Pappalardo, M., Chinè, V., Gismondi, P., Neglia, C., Argentiero, A., Calderaro, A., Prati, A., & Esposito, S. (2020). Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics. Antibiotics, 9(11), 767. https://doi.org/10.3 390/antibiotics9110767
Farhat, F., Athar, M. T., Ahmad, S., Madsen, D. Ø., & Sohail, S. S. (2023). Antimicrobial resistance and machine learning: Past, present, and future. Frontiers in Microbiology,14, 1179312. https://doi.org/10.3389/fmicb.2023.1179312
Farhat, F., Sohail, S. S., & Madsen, D. Ø. (2023a). How trustworthy is ChatGPT? The case of bibliometric analyses. Cogent Engineering, 10(1), 2222988. https://doi.org/10.1080/2 3311916.2023.2222988
Farhat, F., Sohail, S. S., & Madsen, D. Ø. (2023b). How trustworthy is ChatGPT? The case of bibliometric analyses. Cogent Engineering, 10(1), 2222988. https://doi.org/10.1080/2 3311916.2023.2222988
Guo, Y.-M., Huang, Z.-L., Guo, J., Li, H., Guo, X.- R., & Nkeli, M. J. (2019). Bibliometric Analysis on Smart Cities Research. Sustainability, 11(13), 3606.https://doi.org/ 10.3390/su11133606
Karpov, O. E., Pitsik, E. N., Kurkin, S. A., Maksimenko, V. A., Gusev, A. V., Shusharina, N. N., & Hramov, A. E. (2023). Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. International Journal of Environmental Research and Public Health, 20(7), 5335. https://doi.org/10.3390/ijerph20075335
Kim, J. I., Maguire, F., Tsang, K. K., Gouliouris, T., Peacock, S. J., McAllister, T. A., McArthur, A. G., & Beiko, R. G. (2022). Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective. Clinical Microbiology Reviews, 35(3), e00179-21. https://doi.org/10.1128/cmr.00179-21
Kumar, A., Shukla, A., Shinu, P., Mathew, B., Kailkhura, S., Singh, P. P., & Nair, A. B. (2023). Rationalization of Antibiotic Prescription: Modulation of theGut Microbiome and Possibilities of Minimizing the Risks for the Development of Antibiotic Resistance—A Narrative Review. Journal of Pharmacology and Pharmacotherapeuti cs,14(2), 93– 106. https://doi.org/10.1177/ 0976500X231189341
Kumavath, R., Gupta, P., Tatta, E. R., Mohan, M. S., Salim, S. A., & Busi, S. (2025). Unraveling the role of mobile genetic elements in antibiotic resistance transmission and defense strategies in bacteria. Frontiers in Systems Biology, 5, 1557413.https://doi.org/10.3389/fsysb.2025.1557413
Li, H., & Li, B. (2024). The state of metaverse research: A bibliometric visual analysis based on CiteSpace. Journal of Big Data, 11(1), 14. https://doi.org/10.1186/s40537- 024-00877-x
Liao, H., Xie, L., Zhang, N., Lu, J., & Zhang, J. (2025a). Advancements in AI-driven drug sensitivity testing research. Frontiers in Cellular and Infection Microbiology, 15, 1560569.https://doi.org/10.3389/fcimb.2025.1560569
Liao, H., Xie, L., Zhang, N., Lu, J., & Zhang, J. (2025b). Advancements in AI-driven drug sensitivity testing research. Frontiers in Cellular and Infection Microbiology, 15, 1560569. https://doi.org/10.3389/fcimb.20 25.1560569
Martínez-López, F. J., Merigó, J. M., ValenzuelaFernández, L., & Nicolás, C. (2018). Fifty years of the European Journal of Marketing: A bibliometric analysis. European Journal of Marketing, 52(1/2), 439–468. https://doi.org/10.1108/EJM-11- 2017-0853
Mostafaei, A., Akbari, H., Lundborg, C. S., Kabiri, N., Vahed, N., Hajebrahimi, S., & Doshmangir, L. (2023). A systematic synthesis of expert opinion on effective policies to tackle bacterial resistance worldwide. Veterinary Medicine and Science, 9(3), 1395–1406. https://doi.org/10.1002/vms3.1127
Payumo, J., Alocilja, E., Boodoo, C., LuchiniColbry, K., Ruegg, P., McLamore, E., Vanegas, D., Briceno, R., CastanedaSabogal, A., Watanabe, K., Gordoncillo, M., Amalin, D., Fernando, L., & Bhusal, N. (2021). Next Generation of AMR Network. Encyclopedia, 1(3), 871–892. https://doi.org/10.3390/encyclopedia10300 67
Rahit, K. M. T. H., Avramovic, V., Chong, J. X., & Tarailo-Graovac, M. (2024). GPAD: A natural language processing-based application to extract the gene-disease association discovery information from OMIM. BMC Bioinformatics, 25(1), 84. https://doi.org/10.1186/s12859-024- 05693-x
Reperant, L., Mackenzie, J., Venter, M., Mulumba, M., & Osterhaus, A. (2025). Scientific highlights of the 8th world one health Congress, Cape Town, South Africa 2024. One Health Outlook, 7(1), 20, s42522-025- 00143–x. https://doi.org/10.1186/s42522- 025-00143-x
Sakagianni, A., Koufopoulou, C., Feretzakis, G., Kalles, D., Verykios, V. S., Myrianthefs, P., & Fildisis, G. (2023). Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review. Antibiotics, 12(3), 452.https://doi.org/10.33 90/antibiotics12030452
Tang, K. W. K., Millar, B. C., & Moore, J. E. (2023). Antimicrobial Resistance (AMR). British Journal of Biomedical Science, 80, 11387. https://doi.org/10.3389/bjbs.2023.11387
Tang, S., Xu, Y., & Li, X. (2024). Worldwide trend in research on Candida albicans and cancer correlations: A comprehensive bibliometric analysis. Frontiers in Microbiology, 15, 1398527.https://doi.org/10.3389/fmicb.2024.1398527
Wang, Z., Zhu, G., & Li, S. (2025a). Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: Bibliometric and visualization analysis. Frontiers in Medicine,12,1492709.https://doi.org/10.3389/fmed.2025.1492709
Wang, Z., Zhu, G., & Li, S. (2025b). Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: Bibliometric and visualization analysis. Frontiers in Medicine, 12, 1492709.https://doi.org/10.3389/fmed.2025.1492709
Yang, J., Eyre, D. W., & Clifton, D. A. (2023). Interpretable Machine Learning-based Decision Support for Prediction of Antibiotic Resistance for Complicated Urinary Tract Infections. Health Informatics.https://doi.org/10.1101/2023.01.09.23284
Published
2025-12-07
How to Cite
Nurrahma, A., Kusuma, I., Ramadhani, N., Nufus, A., Saputri, A., Haryanti, S., & Putri, R. (2025, December 7). Analisis Bibliometrik Global Pemanfaatan Artificial Intelligence Dalam Penelitian Resistensi Antimikroba Periode 2015–2025. Seminar Nasional Penelitian Dan Pengabdian Kepada Masyarakat 2025, 4(1), 248-256. https://doi.org/https://doi.org/10.35960/snppkm.v4i1.1358
Section
Penelitian
This work is licensed under a Creative Commons Attribution 4.0 International License.
