Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis
ObjectiveTo systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-ma...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1492709/full |
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| author | Zhongli Wang Zhongli Wang Gaopei Zhu Shixue Li Shixue Li |
| author_facet | Zhongli Wang Zhongli Wang Gaopei Zhu Shixue Li Shixue Li |
| author_sort | Zhongli Wang |
| collection | DOAJ |
| description | ObjectiveTo systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.MethodsA comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.ResultsA collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold’s protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.ConclusionThis bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations. |
| format | Article |
| id | doaj-art-e93b5ac947da4b2491cbe48bb20cb983 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-e93b5ac947da4b2491cbe48bb20cb9832025-01-28T05:10:33ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011210.3389/fmed.2025.14927091492709Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysisZhongli Wang0Zhongli Wang1Gaopei Zhu2Shixue Li3Shixue Li4Centre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNHC Key Lab of Health Economics and Policy Research, Shandong University, Jinan, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaCentre for Health Management and Policy Research, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, ChinaNHC Key Lab of Health Economics and Policy Research, Shandong University, Jinan, ChinaObjectiveTo systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.MethodsA comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.ResultsA collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold’s protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.ConclusionThis bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.https://www.frontiersin.org/articles/10.3389/fmed.2025.1492709/fullartificial intelligenceantimicrobial resistanceantibioticsbibliometric analysisdeep learning |
| spellingShingle | Zhongli Wang Zhongli Wang Gaopei Zhu Shixue Li Shixue Li Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis Frontiers in Medicine artificial intelligence antimicrobial resistance antibiotics bibliometric analysis deep learning |
| title | Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis |
| title_full | Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis |
| title_fullStr | Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis |
| title_full_unstemmed | Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis |
| title_short | Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis |
| title_sort | mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance bibliometric and visualization analysis |
| topic | artificial intelligence antimicrobial resistance antibiotics bibliometric analysis deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1492709/full |
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