ArticleEcoHealth2026
Artificial Intelligence in Infectious Disease Research.
Article in EcoHealth, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInfectious diseases remain a major global health challenge, and while AI is rapidly advancing diagnostic techniques such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and ultrasound, its full potential in the detection and management of these diseases has yet to be thoroughly explored. This study examines the global research trends, key contributors, and thematic shifts in the field of AI-driven diagnostics for infectious diseases.
methodsThe publications obtained data from the Web of Science (WoS) database reveal an exponential growth with 5465 publications from 2005 to 2024. The search targeted studies on human, animal, and environmental health using keywords such as "infectious disease," "communicable disease," "environment," "machine learning," and "artificial intelligence" in title, abstract, and keyword fields. Bibliometric parameters such as publication trends, prolific authors, and geographical distribution were assessed, and core journals, institutions were identified, keyword co-occurrence analysis highlighted major research themes. Analyses were conducted using pyBibX, bibliometrix, and VOSviewer to map relationships among authors, journals, and thematic areas.
findingsThe USA leads AI-driven infectious disease research, contributing 1687 publications and 72,732 citations, dominated the combined contributions of China and India, whose total amounts to 1768 publications and 34,927 citations. The Chinese Academy of Science (China), University of Oxford (UK), and Harvard Medical School (USA) emerged as the top institutions. "Scientific Reports" turned out to be the top journal with 157 publications followed by "PLOS One" (144) and "Frontiers in Immunology" (127). Citation network analysis highlighted regional disparities, with low-income countries such as Gambia and Mozambique underrepresented. Keyword clustering identified six key research themes, with machine learning as a central theme across diagnostics, epidemiology, imaging, and drug discovery.
conclusionAI is transforming infectious disease research with significant contributions from high-income countries. However, emerging collaborations in lower middle-income countries highlight the need for equitable AI adoption. Addressing these disparities in infrastructure, funding, technological, and computational capability through global collaborations is important. Future studies should focus on overcoming the real-world implementation challenges to maximize AI's impact in infectious disease research.
Indexed as
Identifiers
42552502What 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.