Artificial intelligence research in journals indexed in the web of science “infectious diseases” category: a bibliometric analysis, 2016–2025

GBD 2023 Lower Respiratory Infections and Antimicrobial Resistance Collaborators. Global burden of lower respiratory infections and aetiologies, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet Infect Dis. 2026;26:343–61. https://doi.org/10.1016/S1473-3099(25)00689-9.

Article  Google Scholar 

GBD 2021 Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Lancet. 2024;404:1199–226. https://doi.org/10.1016/S0140-6736(24)01867-1.

Article  Google Scholar 

Rabaan AA, Bakhrebah MA, Alotaibi J, et al. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. J Infect Public Health. 2023;16:1837–47.

Article  PubMed  Google Scholar 

Pugliese R, Regondi S, Marini R. Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Sci Manage. 2021;4:19–29.

Article  Google Scholar 

Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689.

Article  PubMed  PubMed Central  Google Scholar 

Atalay S, Sönmez U. Digital twin in health care. In: Karaarslan E, Aydin Ö, Cali Ü, editors. Digital twin driven intelligent systems and emerging metaverse. Singapore: Springer; 2023. p. 209–31.

Chapter  Google Scholar 

Al Kuwaiti A, Nazer K, Al-Reedy A, et al. A review of the role of artificial intelligence in healthcare. J Pers Med. 2023;13:951.

Article  PubMed  PubMed Central  Google Scholar 

Assudani PJ, Bhurgy AS, Kollem S, et al. Artificial intelligence and machine learning in infectious disease diagnostics: a comprehensive review of applications, challenges, and future directions. Microchem J. 2025;218:115802.

Article  CAS  Google Scholar 

Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology. 2020;296:E65–71.

Article  PubMed  PubMed Central  Google Scholar 

Abubaker Bagabir S, Ibrahim NK, Abubaker Bagabir H, Hashem Ateeq R. Covid-19 and artificial intelligence: genome sequencing, drug development and vaccine discovery. J Infect Public Health. 2022;15:289–96.

Article  PubMed  PubMed Central  Google Scholar 

Aria M, Cuccurullo C. Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr. 2017;11:959–75.

Article  Google Scholar 

Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56. https://doi.org/10.1038/s41591-018-0300-7.

Article  CAS  PubMed  Google Scholar 

Chang Z, Zhan Z, Zhao Z, et al. Application of artificial intelligence in COVID-19 medical area: a systematic review. J Thorac Dis. 2021;13:7034–53. https://doi.org/10.21037/jtd-21-747.

Article  PubMed  PubMed Central  Google Scholar 

Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: survey study. J Med Internet Res. 2020;22:e17620. https://doi.org/10.2196/17620.

Article  PubMed  PubMed Central  Google Scholar 

Zhang K, Liu X, Shen J, et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell. 2020;181:1423–e143311. https://doi.org/10.1016/j.cell.2020.04.045.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zheng N, Du S, Wang J, et al. Predicting COVID-19 in China using hybrid AI model. IEEE Trans Cybern. 2020;50:2891–904. https://doi.org/10.1109/TCYB.2020.2990162.

Article  PubMed  Google Scholar 

Limon Ö, Bayram B, Çetin M, Limon G, Dirican N. A bibliometric analysis of clinical studies on artificial intelligence in emergency medicine. Medicine (Baltimore). 2025;104:e43282. https://doi.org/10.1097/MD.0000000000043282.

Article  PubMed  PubMed Central  Google Scholar 

Luo Z, Lv J, Zou K. A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study. Front Med (Lausanne). 2025;12:1553970. https://doi.org/10.3389/fmed.2025.1553970.

Article  PubMed  PubMed Central  Google Scholar 

Wang G, Meng X, Zhang F. Past, present, and future of global research on artificial intelligence applications in dermatology: a bibliometric analysis. Medicine (Baltimore). 2023;102:e35993. https://doi.org/10.1097/MD.0000000000035993.

Article  PubMed  PubMed Central  Google Scholar 

Celi LA, Cellini J, Charpignon ML, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities-a global review. PLoS Digit Health. 2022;1:e0000022. https://doi.org/10.1371/journal.pdig.0000022.

Article  PubMed  PubMed Central  Google Scholar 

Singh D, Kumar V, Vaishali, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis. 2020;39:1379–89. https://doi.org/10.1007/s10096-020-03901-z.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tang M, Mu F, Cui C, et al. Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis. Front Med (Lausanne). 2023;9:1043589. https://doi.org/10.3389/fmed.2022.1043589.

Article  PubMed  PubMed Central  Google Scholar 

Cabanillas-Lazo M, Quispe-Vicuña C, Pascual-Guevara M, et al. Bibliometric analyses of applications of artificial intelligence on tuberculosis. Int J Mycobacteriol. 2022;11:389–93. https://doi.org/10.4103/ijmy.ijmy_134_22.

Article  PubMed  Google Scholar 

Hwang EJ, Park S, Jin KN, et al. Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs. Clin Infect Dis. 2019;69:739–47. https://doi.org/10.1093/cid/ciy967.

Article  PubMed  PubMed Central  Google Scholar 

Guo P, Liu T, Zhang Q, et al. Developing a dengue forecast model using machine learning: a case study in China. PLoS Negl Trop Dis. 2017;11:e0005973. https://doi.org/10.1371/journal.pntd.0005973.

Article  PubMed  PubMed Central  Google Scholar 

Benedum CM, Seidahmed OME, Eltahir EAB, Markuzon N. Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore. PLoS Negl Trop Dis. 2018;12:e0006935. https://doi.org/10.1371/journal.pntd.0006935.

Article  PubMed  PubMed Central  Google Scholar 

Marcus JL, Hurley LB, Krakower DS, Alexeeff S, Silverberg MJ, Volk JE. Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study. Lancet HIV. 2019;6:e688–95. https://doi.org/10.1016/S2352-3018(19)30137-7.

Article  PubMed  PubMed Central  Google Scholar 

Li C, Ye G, Jiang Y, Wang Z, Yu H, Yang M. Artificial intelligence in battling infectious diseases: a transformative role. J Med Virol. 2024;96:e29355. https://doi.org/10.1002/jmv.29355.

Article  CAS  PubMed  Google Scholar 

MacIntyre CR, Chen X, Kunasekaran M, et al. Artificial intelligence in public health: the potential of epidemic early warning systems. J Int Med Res. 2023;51:3000605231159335. https://doi.org/10.1177/03000605231159335.

Article  PubMed  PubMed Central  Google Scholar 

Ezeh CJ, Anioke SC, Oyewole S, David MG. The role of predictive analytics in enhancing public health surveillance: Proactive and data-driven interventions. World J Adv Res Reviews. 2024;24:3059–77. https://doi.org/10.30574/wjarr.2024.24.3.3909.

Article  Google Scholar 

Liscano Y, Anillo Arrieta LA, Montenegro JF, Prieto-Alvarado D, Ordoñez J. Early warning of infectious disease outbreaks using social media and digital data: a scoping review. Int J Environ Res Public Health. 2025;22:1104. https://doi.org/10.3390/ijerph22071104.

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