ArticleInfection2026
Artificial intelligence research in journals indexed in the web of science "infectious diseases" category: a bibliometric analysis, 2016-2025.
Article in Infection, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence (AI) is increasingly being applied in the field of infectious diseases. This study aimed to characterize publication trends, collaboration networks, and thematic patterns among AI-related original articles published between 2016 and 2025 in journals assigned to the Web of Science Core Collection (WoSCC) "Infectious Diseases" category.
methodsA systematic bibliometric analysis was performed using the WoSCC. The query combined AI-related terms (machine learning, deep learning, neural networks, large language models, and allied concepts) restricted to the WoSCC category of "Infectious Diseases" and an English-language filter, yielding 1,252 original articles published between January 2016 and December 2025. Bibliometric computations and visualizations were conducted using the Bibliometrix R package, VOSviewer, and Scimago Graphica. Keyword co-occurrence network analysis and temporal trend mapping were employed to identify thematic clusters and emerging research foci.
resultsThe 1,252 articles were contributed by 9,160 authors from 124 countries across 2,850 institutions and published in 125 journals. Annual output grew more than 30-fold between 2016 and 2025, with 84.7% of all publications appearing in the final five years. The United States and China were the most productive countries and collectively dominated international collaboration networks. Harvard University was the leading institution. Keyword network analysis identified nine distinct thematic clusters. Recent trend analyses reveal a significant shift towards clinical applications, specifically highlighting antimicrobial resistance surveillance and early sepsis prediction as dominant research hotspots.
conclusionScientific production on AI applications in infectious diseases has expanded exponentially over the past decade, catalyzed principally by the COVID-19 pandemic. Sepsis management, antimicrobial resistance, surveillance, and clinical prediction represent the most prominent research themes. These patterns indicate areas of growing scientific attention. This study may help guide future clinical validation and implementation research.
Indexed as
Identifiers
42593639What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.