Análise Bibliométrica sobre a Inteligência Artificial aplicada no Controle de Infecções
DOI:
https://doi.org/10.26694/repis.v11i1.7237Palavras-chave:
Indicadores Bibliométricos, Doenças Infecciosas, Epidemiologia, Controle de infecção, Inteligência artificialResumo
Introdução: As doenças infecciosas continuam sendo um desafio global significativo, especialmente em contextos hospitalares. A inteligência artificial (IA) tem se destacado como ferramenta promissora no diagnóstico, vigilância e controle dessas infecções. Objetivo: Realizar uma análise bibliométrica da produção científica sobre o uso da IA no controle de infecções. Metodologia: Trata-se de um estudo bibliométrico, descritivo, com abordagem quantitativa, baseado em dados da Web of Science™ Core Collection. A busca foi realizada em agosto de 2024 e resultou em 1.189 artigos. As análises foram conduzidas no RStudio (pacote Bibliometrix e Biblioshiny), VOSviewer e CiteSpace. Foram avaliados aspectos como evolução temporal das publicações, autores mais produtivos, instituições, países, periódicos e estrutura temática. Resultados: Observou-se crescimento expressivo da produção científica a partir de 2016, com pico em 2023. Os artigos foram publicados por 7.058 autores, em 624 periódicos, com destaque para Scientific Reports e PLOS ONE. A China e os Estados Unidos lideraram em número de publicações. As principais instituições incluíram University of California System e Wuhan University. A análise temática revelou quatro clusters principais: diagnóstico/prognóstico, vigilância epidemiológica, cuidados críticos (como sepse) e apoio à decisão clínica. Implicações: A bibliometria demonstrou a consolidação da IA como ferramenta estratégica no enfrentamento de infecções, especialmente após a pandemia de COVID-19. O estudo aponta a necessidade de ampliar a participação de países de baixa e média renda, realizar validações multicêntricas e investigar a aplicabilidade clínica das soluções baseadas em IA.
Referências
Zhao Y, Li J, Li X, Xu J. Application of artificial intelligence in the prevention and control of
infectious diseases. China CDC Wkly. 2024; 6 (30): 642-6. doi:10.46234/ccdcw2024.192.
Chen Y, Li Y, Narayan S, Subramanian N, Roland T. Application of artificial intelligence in infectious
disease management: advances and prospects. Front Pharmacol. 2022; 13: 1079566.
doi:10.3389/fphar.2022.1079566.
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present
and future. Stroke Vasc Neurol. 2017; 2 (4): 230-43. doi:10.1136/svn-2017-000101.
He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence
technologies in medicine. Nat Med. 2019; 25 (1): 30-6. doi:10.1038/s41591-018-0307-0.
Ferreira VLR, Silva Júnior JB, Garcia LP. Inteligência artificial aplicada ao enfrentamento de
epidemias: revisão e perspectivas. Rev Saude Publica. 2020; 54: 80. doi:10.11606/s1518-
2020054002475.
Hutchins BI, Yuan X, Anderson JM, Santangelo GM. Relative Citation Ratio (RCR): a new metric that
uses citation rates to measure influence at the article level. PLoS Biol. 2016;14 (9): e1002541.
Vošner HB, Kokol P, Bobek S, Železnik D, Završnik J. A bibliometric retrospective of the Journal
Computers in Human Behavior (1991–2015). Comput Human Behav. 2016;65:46-58.
Zupic I, Čater T. Bibliometric methods in management and organization. Organizational Research
Methods. 2015;18(3):429-472.
Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: anoverview and guidelines. J Bus Res. 2021;133:285–96.
Koo M, Lin SC. An analysis of reporting practices in the top 100 cited health and medicine-related
bibliometric studies from 2019 to 2021 based on a proposed guidelines. Heliyon. 2023 May 27;9(6):e16780.
Moura MEB, et al. Global trends from original research on COVID-19 and coinfection. Revista
Prevenção de Infecção e Saúde. 2022;8(1).
Julien A, et al. Analysis of scientific publications related to Hepatitis B prevention in the last
decade: 2013-2023. Revista Prevenção de Infecção e Saúde. 2024;10(1).
Lin HY, Deng X, Song D. Research trends of global artificial intelligence application in obstetrics
and gynecology from 1999 to 2025: a bibliometric analysis based on Web of Science. J Robot Surg.
;19(1):606.
Portela DMMC, et al. Treatment of Parkinson's disease by deep brain stimulation: a bibliometric
analysis. Sao Paulo Med J. 2024;142(5):e2023187.
Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J
Informetrics. 2017;11(4):959-975.
Büyükkıdık S. A bibliometric analysis: A tutorial for the bibliometrix package in R using IRT
literature. J Meas Eval Educ Psychol. 2022;13(3):164-193.
van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping.
Scientometrics. 2010;84:523–538.
Goodacre S, Rooney K, Slater K, et al. Rapid identification of urinary tract infection bacteria using
hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiol-Sgm. 1998;144:339–
Peiffer-Smadja N. Machine learning for clinical decision support in infectious diseases: a narrative
review of current applications. Clin Microbiol Infect. 2020;26:300–310.
Hu S, Gao Y, Wang L, et al. Weakly Supervised Deep Learning for COVID-19 Infection Detection and
Classification From CT Images. IEEE Access. 2020;8:219–225.
Dechant R, Poland S, Wisser RJ, et al. Automated Identification of Northern Leaf Blight-Infected
Maize Plants from Field Imagery Using Deep Learning. Phytopathology. 2017;107:210–220.
Shichijo S. Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori
Infection Based on Endoscopic Images. Ebiomedicine. 2017;18:204–213.
Alakus TB, et al. Comparison of deep learning approaches to predict COVID-19 infection. Chaos
Soliton Fract. 2020;139:195–205.
Brinati D, et al. Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning:
A Feasibility Study. J Med Syst. 2020; 44:190.
Chae S, et al. Predicting Infectious Disease Using Deep Learning and Big Data. Int J Environ Res
Public Health. 2018;15:172.
Singh D, et al. Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected
by Anthracnose Disease. IEEE Access. 2019;7:172–184.27. Hassantabar S, et al. Diagnosis and detection of infected tissue of COVID-19 patients based on lung
x-ray image using convolutional neural network approaches. Chaos Soliton Fract. 2020;138:162–172.
Sedik A, et al. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection
of COVID-19 Infections. Viruses. 2020;12:132.
Taylor RA, et al. Predicting urinary tract infections in the emergency department with machine
learning. PLOS ONE. 2018;13:e126.
Itoh T, et al. Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal
endoscopy images. Endosc Int Open. 2018;6:123–130.
Majid M, et al. Classification of stomach infections: A paradigm of convolutional neural network
along with classical features fusion and selection. Microsc Res Tech. 2020;83:121–131.
Wong A, et al. Leveraging artificial intelligence in the fight against infectious diseases. Science.
;379:120–130.
Chen C, Dubin R, Kim MC. Emerging trends and new developments in regenerative medicine: a
scientometric update (2000–2014). Expert Opin Biol Ther. 2014;14(9):1295-317. doi:
1517/14712598.2014.920813.
Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An
overview and guidelines. J Bus Res. 2021;133:285-96. doi: 10.1016/j.jbusres.2021.04.070.
Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19
pandemic. Diabetes Metab Syndr. 2020;14(4):337-339. doi: 10.1016/j.dsx.2020.04.012.
L Arab R, Adel R, et al. Inteligência artificial na prevenção de infecções hospitalares: uma revisão
integrativa. Front Public Health. 2025;13:1547-450.
Pham Q-V, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial intelligence (AI) and big
data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE Access. 2020;8:130820-
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat
Med. 2019;25(1):44-56.
Piwowar H, Priem J, Larivière V, Alperin JP, Matthias L, Norlander B, et al. The state of OA: a largescale analysis of the prevalence and impact of Open Access articles. PeerJ. 2018;6:e4375.
Kitajima K, et al. The altering landscape of US–China science collaboration: From convergence to
divergence. Palgrave Communications / Nature (Humanities & Social Sciences Comms) 2025;12.
Zhu Y, Kim D, Yan E, Kim MC, Qi G. Analyzing China’s research collaboration with the United States
in high-impact and high-technology research. Quantitative Science Studies. 2021;2(1):363-375.
Xie Y, Zhai Y, Lu G. Evolution of artificial intelligence in healthcare: a 30-year bibliometric study.
Front Med (Lausanne). 2025;11:1505692.
Kitajima K, Okamura K. Evolving landscape of US-China science collaboration: Convergence and
divergence. arXiv [Preprint]. 2023; arXiv:2309.05033.44. Chen J, Zhang L, Hu J, Liu L, Chen W. Use of an artificial neural network to predict risk factors of
nosocomial infection in lung cancer patients. Asian Pac J Cancer Prev. 2014;15(13):5349-5353.
Price DJ de S. Little Science, Big Science. New York: Columbia University Press; 1963.
Cao S, Wei Y, Yue Y, Wang D, Xiong A, Yang J, Zeng H. Uncovering the scientific landscape: A
bibliometric and visualized analysis of artificial intelligence in Traditional Chinese Medicine. Heliyon.
;10(18):e37439.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2025 Ellayne Beatriz Nunes de Sousa , Lorrana Maria Araújo de Almeida Nobre, Elisa Linhares Lima, Ana Raquel Batista de Carvalho
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
