{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:34:49Z","timestamp":1788413689016,"version":"build-2803163510"},"reference-count":114,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,2]],"date-time":"2025-03-02T00:00:00Z","timestamp":1740873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Antibiotics"],"abstract":"<jats:p>Background\/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI\u2019s role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O\u2019Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.<\/jats:p>","DOI":"10.3390\/antibiotics14030256","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T05:52:16Z","timestamp":1740981136000},"page":"256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial Intelligence in Bacterial Infections Control: A Scoping Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1101-5840","authenticated-orcid":false,"given":"Rasha","family":"Abu-El-Ruz","sequence":"first","affiliation":[{"name":"College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6121-5158","authenticated-orcid":false,"given":"Mohannad Natheef","family":"AbuHaweeleh","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5655-7367","authenticated-orcid":false,"given":"Ahmad","family":"Hamdan","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3621-0518","authenticated-orcid":false,"given":"Humam Emad","family":"Rajha","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jood Mudar","family":"Sarah","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Jordan, Amman P.O. Box 11942, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1977-2222","authenticated-orcid":false,"given":"Kaoutar","family":"Barakat","sequence":"additional","affiliation":[{"name":"College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7487-8770","authenticated-orcid":false,"given":"Susu M.","family":"Zughaier","sequence":"additional","affiliation":[{"name":"College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gilbert, G.L., and Kerridge, I. (2020). Hospital Infection Prevention and Control (IPC) and Antimicrobial Stewardship (AMS): Dual Strategies to Reduce Antibiotic Resistance (ABR) in Hospitals. Ethics and Drug Resistance: Collective Responsibility for Global Public Health, Springer Nature.","DOI":"10.1007\/978-3-030-27874-8_6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13756-016-0149-9","article-title":"Core components for effective infection prevention and control programmes: New WHO evidence-based recommendations","volume":"6","author":"Storr","year":"2017","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/s13756-019-0527-1","article-title":"Implementation research for the prevention of antimicrobial resistance and healthcare-associated infections; 2017 Geneva infection prevention and control (IPC)-think tank (part 1)","volume":"8","author":"Zingg","year":"2019","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_4","unstructured":"WHO (2024, June 09). IPC and Antimicrobial Resistance (AMR). Available online: https:\/\/www.who.int\/teams\/integrated-health-services\/infection-prevention-control\/ipc-and-antimicrobial-resistance#:~:text=Strong%20IPC%20is%20a%20the,hygiene%20and%20better%20hospital%20hygiene."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.jiph.2020.06.006","article-title":"Artificial intelligence-based tools to control healthcare associated infections: A systematic review of the literature","volume":"13","author":"Scardoni","year":"2020","journal-title":"J. Infect. Public. Health"},{"key":"ref_6","first-page":"e19962","article-title":"Incidence, Risk Factors, and Mortality From Hospital-Acquired Infections at a Hospital in Mauritius","volume":"13","author":"Nuckchady","year":"2021","journal-title":"Cureus"},{"key":"ref_7","unstructured":"Sikora, A., and Zahra, F. (2023). Nosocomial Infections. StatPearls [Internet], StatPearls Publishing."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5106","DOI":"10.1128\/IAI.00376-06","article-title":"Uropathogenic Escherichia coli induces extrinsic and intrinsic cascades to initiate urothelial apoptosis","volume":"74","author":"Klumpp","year":"2006","journal-title":"Infect. Immun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"282","DOI":"10.12968\/bjcn.2011.16.6.282","article-title":"Infection control in the community: A pragmatic approach","volume":"16","author":"Swanson","year":"2011","journal-title":"Br. J. Community Nurs."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"S15","DOI":"10.1016\/j.cmi.2015.11.004","article-title":"Laboratory diagnosis, clinical management and infection control of the infections caused by extensively drug-resistant Gram-negative bacilli: A Chinese consensus statement","volume":"22","author":"Guan","year":"2016","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"S238","DOI":"10.1093\/cid\/ciw012","article-title":"Infection control for drug-resistant tuberculosis: Early diagnosis and treatment is the key","volume":"62","author":"Isaakidis","year":"2016","journal-title":"Clin. Infect. Dis."},{"key":"ref_12","unstructured":"Wilson, J. (2019). Infection Control in Clinical Practice Updated Edition, Elsevier Health Sciences."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Arzilli, G., De Vita, E., Pasquale, M., Carloni, L.M., Pellegrini, M., Di Giacomo, M., Esposito, E., Porretta, A.D., and Rizzo, C. (2024). Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review. Antibiotics, 13.","DOI":"10.3390\/antibiotics13010077"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"901","DOI":"10.46234\/ccdcw2024.192","article-title":"The Implications of Artificial Intelligence on Infection Prevention and Control: Current Progress and Future Perspectives","volume":"6","author":"Yang","year":"2024","journal-title":"China CDC Wkly."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Maleki Varnosfaderani, S., and Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11.","DOI":"10.3390\/bioengineering11040337"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tsitou, V.-M., Rallis, D., Tsekova, M., and Yanev, N. (2024). Microbiology in the era of artificial intelligence: Transforming medical and pharmaceutical microbiology. Biotechnol. Biotechnol. Equip., 38.","DOI":"10.1080\/13102818.2024.2349587"},{"key":"ref_17","unstructured":"Chindelevitch, L., Jauneikaite, E., Wheeler, N.E., Allel, K., Ansiri-Asafoakaa, B.Y., Awuah, W.A., Bauer, D.C., Beisken, S., Fan, K., and Grant, G. (2022). Applying data technologies to combat AMR: Current status, challenges, and opportunities on the way forward. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e40238","DOI":"10.2196\/40238","article-title":"Artificial Intelligence Applications in Health Care Practice: Scoping Review","volume":"24","author":"Sharma","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jinf.2023.07.006","article-title":"Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician","volume":"87","author":"Theodosiou","year":"2023","journal-title":"J. Infect."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1055\/s-0039-1677692","article-title":"Deep learning versus conventional machine learning for detection of healthcare-associated infections in French clinical narratives","volume":"58","author":"Rabhi","year":"2019","journal-title":"Methods Inf. Med."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jeon, K., Kim, J.M., Rho, K., Jung, S.H., Park, H.S., and Kim, J.S. (2022). Performance of a Machine Learning-Based Methicillin Resistance of Staphylococcus aureus Identification System Using MALDI-TOF MS and Comparison of the Accuracy according to SCCmec Types. Microorganisms, 10.","DOI":"10.3390\/microorganisms10101903"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"\u00c7a\u01e7layan, \u00c7., Barnes, S.L., Pineles, L.L., Harris, A.D., and Klein, E.Y. (2022). A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.853757"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.1017\/ice.2023.54","article-title":"Clostridioides difficile infection surveillance in intensive care units and oncology wards using machine learning","volume":"44","author":"Balczewski","year":"2023","journal-title":"Infect. Control Hosp. Epidemiol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aggarwal, S., Dhall, A., Patiyal, S., Choudhury, S., Arora, A., and Raghava, G.P.S. (2023). An ensemble method for prediction of phage-based therapy against bacterial infections. Front. Microbiol., 14.","DOI":"10.3389\/fmicb.2023.1148579"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1093\/aje\/kwac117","article-title":"Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters","volume":"191","author":"Althomsons","year":"2022","journal-title":"Am. J. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Aminian, M., Couvin, D., Shabbeer, A., Hadley, K., Enberg, S., Rastogi, N., and Bennett, K.P. (2014). Predicting mycobacterium tuberculosis complex clades using knowledge-based bayesian networks. BioMed Res. Int., 2014.","DOI":"10.1155\/2014\/398484"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1017\/ice.2022.66","article-title":"Extending outbreak investigation with machine learning and graph theory: Benefits of new tools with application to a nosocomial outbreak of a multidrug-resistant organism","volume":"44","author":"Atkinson","year":"2023","journal-title":"Infect. Control Hosp. Epidemiol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Az\u00e9, J., Sola, C., Zhang, J., Lafosse-Marin, F., Yasmin, M., Siddiqui, R., Kremer, K., Van Soolingen, D., and Refr\u00e9gier, G. (2015). Genomics and machine learning for taxonomy consensus: The mycobacterium tuberculosis complex paradigm. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0130912"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bournez, C., Riool, M., de Boer, L., Cordfunke, R.A., de Best, L., van Leeuwen, R., Drijfhout, J.W., Zaat, S.A.J., and van Westen, G.J.P. (2023). CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics, 12.","DOI":"10.3390\/antibiotics12040725"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"161.e161","DOI":"10.1016\/j.cmi.2015.10.009","article-title":"Automated categorization of methicillin-resistant Staphylococcus aureus clinical isolates into different clonal complexes by MALDI-TOF mass spectrometry","volume":"22","author":"Camoez","year":"2016","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cheah, A.L.Y., Cheng, A.C., Spelman, D., Nation, R.L., Kong, D.C.M., and McBryde, E.S. (2018). Mathematical modelling of vancomycin-resistant enterococci transmission during passive surveillance and active surveillance with contact isolation highlights the need to identify and address the source of acquisition. BMC Infect. Dis., 18.","DOI":"10.1186\/s12879-018-3388-y"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1021\/cb800240j","article-title":"Use of artificial intelligence in the design of small peptide antibiotics effective against a broad spectrum of highly antibiotic-resistant superbugs","volume":"4","author":"Cherkasov","year":"2009","journal-title":"ACS Chem. Biol."},{"key":"ref_33","first-page":"16","article-title":"Arden Syntax MLM Building Blocks for Microbiological Concepts and Their Application in Infection Surveillance","volume":"236","author":"Koller","year":"2017","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Doan, T.N., Kong, D.C.M., Marshall, C., Kirkpatrick, C.M.J., and McBryde, E.S. (2015). Characterising the transmission dynamics of Acinetobacter baumannii in intensive care units using hidden markov models. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0132037"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Feng, C., Di, J., Jiang, S., Li, X., and Hua, F. (2023). Machine learning models for prediction of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus: A singled centered retrospective study. BMC Infect. Dis., 23.","DOI":"10.1186\/s12879-023-08235-7"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e13920","DOI":"10.1111\/tid.13920","article-title":"Prediction models for carbapenem-resistant Enterobacterales carriage at liver transplantation: A multicenter retrospective study","volume":"24","author":"Freire","year":"2022","journal-title":"Transpl. Infect. Dis."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1017\/ice.2019.42","article-title":"Predicting probability of perirectal colonization with carbapenem-resistant Enterobacteriaceae (CRE) and other carbapenem-resistant organisms (CROs) at hospital unit admission","volume":"40","author":"Goodman","year":"2019","journal-title":"Infect. Control Hosp. Epidemiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"0099","DOI":"10.34133\/hds.0099","article-title":"Detection of Patients at Risk of Multidrug-Resistant Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study","volume":"3","author":"Gouareb","year":"2023","journal-title":"Health Data Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hattori, S., Sekido, R., Leong, I.W., Tsutsui, M., Arima, A., Tanaka, M., Yokota, K., Washio, T., Kawai, T., and Okochi, M. (2020). Machine learning-driven electronic identifications of single pathogenic bacteria. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-72508-3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1086\/588588","article-title":"Validation study of artificial neural network models for prediction of methicillin-resistant Staphylococcus aureus carriage","volume":"29","author":"Hsu","year":"2008","journal-title":"Infect. Control Hosp. Epidemiol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104242","DOI":"10.1016\/j.ijmedinf.2020.104242","article-title":"The detection of hospitalized patients at risk of testing positive to multi-drug resistant bacteria using MOCA-I, a rule-based \u201cwhite-box\u201d classification algorithm for medical data","volume":"142","author":"Jacques","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14604582241234232","DOI":"10.1177\/14604582241234232","article-title":"A study on the risk stratification for patients within 24 hours of admission for risk of hospital-acquired urinary tract infection using Bayesian network models","volume":"30","author":"Jakobsen","year":"2024","journal-title":"Health Informatics J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4722","DOI":"10.1128\/AAC.00075-16","article-title":"Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa","volume":"60","author":"Khaledi","year":"2016","journal-title":"Antimicrob. Agents Chemother."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e10264","DOI":"10.15252\/emmm.201910264","article-title":"Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics","volume":"12","author":"Khaledi","year":"2020","journal-title":"EMBO Mol. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1128\/msystems.00177-21","article-title":"Patient and microbial genomic factors associated with carbapenem-resistant klebsiella pneumoniae extraintestinal colonization and infection","volume":"6","author":"Lapp","year":"2021","journal-title":"mSystems"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liang, Q.Q., Ding, S., Chen, J., Chen, X.Y., Xu, Y.S., Xu, Z.J., and Huang, M. (2024). Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02504-4"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.jgar.2022.03.019","article-title":"Early prediction of carbapenem-resistant Gram-negative bacterial carriage in intensive care units using machine learning","volume":"29","author":"Liang","year":"2022","journal-title":"J. Glob. Antimicrob. Resist."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lyu, J.W., Zhang, X.D., Tang, J.W., Zhao, Y.H., Liu, S.L., Zhao, Y., Zhang, N., Wang, D., Ye, L., and Chen, X.L. (2023). Rapid prediction of multidrug-resistant klebsiella pneumoniae through deep learning analysis of sers spectra. Microbiol. Spectr., 11.","DOI":"10.1128\/spectrum.04126-22"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Marra, A.R., Alzunitan, M., Abosi, O., Edmond, M.B., Street, W.N., Cromwell, J.W., and Salinas, J.L. (2020). Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital. Diagn. Microbiol. Infect. Dis., 98.","DOI":"10.1016\/j.diagmicrobio.2020.115104"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5236168","DOI":"10.1155\/2023\/5236168","article-title":"Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data","volume":"2023","author":"Noman","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.ajic.2021.11.012","article-title":"A comparative analysis of machine learning approaches to predict C. difficile infection in hospitalized patients","volume":"50","author":"Panchavati","year":"2022","journal-title":"Am. J. Infect. Control"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ratzinger, F., Dedeyan, M., Rammerstorfer, M., Perkmann, T., Burgmann, H., Makristathis, A., Dorffner, G., Loetsch, F., Blacky, A., and Ramharter, M. (2015). Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and Gram-negative Bacteremia. Sci. Rep., 5.","DOI":"10.1038\/srep16008"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s13756-022-01174-z","article-title":"Validating administrative data to identify complex surgical site infections following cardiac implantable electronic device implantation: A comparison of traditional methods and machine learning","volume":"11","author":"Leal","year":"2022","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1128\/aac.01023-22","article-title":"Machine Learning to Stratify Methicillin-Resistant Staphylococcus aureus Risk among Hospitalized Patients with Community-Acquired Pneumonia","volume":"67","author":"Rhodes","year":"2023","journal-title":"Antimicrob. Agents Chemother."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"109025","DOI":"10.1016\/j.isci.2024.109025","article-title":"Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis","volume":"27","author":"Sambarey","year":"2024","journal-title":"iScience"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jcrc.2018.01.022","article-title":"Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach","volume":"45","author":"Savin","year":"2018","journal-title":"J. Crit. Care"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Schinkel, M., Boerman, A.W., Bennis, F.C., Minderhoud, T.C., Lie, M., Peters-Sengers, H., Holleman, F., Schade, R.P., de Jonge, R., and Wiersinga, W.J. (2022). Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool. EBioMedicine, 82.","DOI":"10.1016\/j.ebiom.2022.104176"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Seheult, J.N., Stram, M.N., Contis, L., Pontzer, R.E., Hardy, S., Wertz, W., Baxter, C.M., Ondras, M., Kip, P.L., and Snyder, G.M. (2023). Development, Evaluation, and Multisite Deployment of a Machine Learning Decision Tree Algorithm To Optimize Urinalysis Parameters for Predicting Urine Culture Positivity. J. Clin. Microbiol., 61.","DOI":"10.1128\/jcm.00291-23"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1302\/0301-620X.102B7.BJJ-2019-1628.R1","article-title":"2020 Frank Stinchfield Award: Identifying who will fail following irrigation and debridement for prosthetic joint infection","volume":"102","author":"Shohat","year":"2020","journal-title":"Bone Joint J."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"102399","DOI":"10.1016\/j.tube.2023.102399","article-title":"Predictive biomarkers for latent Mycobacterium tuberculosis infection","volume":"147","author":"Singh","year":"2024","journal-title":"Tuberculosis"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"E638","DOI":"10.1093\/cid\/ciaa1887","article-title":"Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance","volume":"73","author":"Sundermann","year":"2021","journal-title":"Clin. Infect. Dis."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cmi.2019.05.013","article-title":"Estimating the association between antibiotic exposure and colonization with extended-spectrum \u03b2-lactamase-producing Gram-negative bacteria using machine learning methods: A multicentre, prospective cohort study","volume":"26","author":"Tacconelli","year":"2020","journal-title":"Clin. Microbiol. Infect."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e41207","DOI":"10.2196\/41207","article-title":"Association Among Household Water, Sanitation, and Hygiene (WASH) Status and Typhoid Risk in Urban Slums: Prospective Cohort Study in Bangladesh","volume":"9","author":"Tadesse","year":"2023","journal-title":"JMIR Public Health Surveill."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1097\/SHK.0000000000002075","article-title":"A preventive tool for predicting bloodstream infections in children with burns","volume":"59","author":"Tsurumi","year":"2023","journal-title":"Shock"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, H.Y., Lee, T.Y., Tseng, Y.J., Liu, T.P., Huang, K.Y., Chang, Y.T., Chen, C.H., and Lu, J.J. (2018). A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194289"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e5784","DOI":"10.7717\/peerj.5784","article-title":"Application of a MALDI-TOF analysis platform (ClinProTools) for rapid and preliminary report of MRSA sequence types in Taiwan","volume":"6","author":"Wang","year":"2018","journal-title":"PeerJ"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s40560-023-00695-y","article-title":"A deep learning model for predicting multidrug-resistant organism infection in critically ill patients","volume":"11","author":"Wang","year":"2023","journal-title":"J. Intensive Care"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.jhin.2011.01.016","article-title":"Role of overcrowding in meticillin-resistant Staphylococcus aureus transmission: Bayesian network analysis for a single public hospital","volume":"78","author":"Waterhouse","year":"2011","journal-title":"J. Hosp. Infect."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13756-023-01294-0","article-title":"Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: A multicenter cohort study","volume":"12","author":"Wu","year":"2023","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"8739447","DOI":"10.1155\/2022\/8739447","article-title":"Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing","volume":"2022","author":"Yan","year":"2022","journal-title":"J. Healthc. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s12931-023-02624-x","article-title":"Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii","volume":"25","author":"Zeng","year":"2024","journal-title":"Respir. Res."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Wang, X., Li, J., Zhang, B., Yan, L., Xu, S., Chen, G., and Gao, H. (2022). A study on the diagnosis of the Helicobacter pylori coccoid form with artificial intelligence technology. Front. Microbiol., 13.","DOI":"10.3389\/fmicb.2022.1008346"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Zwerwer, L.R., Luz, C.F., Soudis, D., Giudice, N., Nijsten, M.W.N., Glasner, C., Renes, M.H., and Sinha, B. (2024). Identifying the need for infection-related consultations in intensive care patients using machine learning models. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-52741-w"},{"key":"ref_74","unstructured":"Roser, M.P.A., Hasell, J., Ritchie, H., Ortiz-Ospina, E., and World Bank Income Groups (2023). World Bank Income Groups, The World Bank."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e0138421","DOI":"10.1128\/msystems.01384-21","article-title":"Genomic Diversity of Hospital-Acquired Infections Revealed through Prospective Whole-Genome Sequencing-Based Surveillance","volume":"7","author":"Mustapha","year":"2022","journal-title":"mSystems"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Gouareb, R., Bornet, A., Proios, D., Pereira, S.G., and Teodoro, D. (2023). Detection of Patients at Risk of Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study. medRxiv.","DOI":"10.1101\/2023.06.01.23290386"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Seheult, J.N., Stram, M.N., Contis, L., Pontzer, R.E., Hardy, S., Wertz, W., Baxter, C.M., Ondras, M., Kip, P.L., and Snyder, G.M. (2023). Development and Evaluation of a Machine Learning Recursive Partitioning Decision Tree Algorithm to Optimize Urinalysis Parameters to Predict Urine Culture Positivity. bioRxiv.","DOI":"10.1101\/2023.03.10.532117"},{"key":"ref_78","first-page":"35","article-title":"Ten golden rules for optimal antibiotic use in hospital settings: The WARNING call to action","volume":"18","author":"Sartelli","year":"2023","journal-title":"World J. Emerg. Surg."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s40506-020-00216-7","article-title":"Using artificial intelligence in infection prevention","volume":"12","author":"Fitzpatrick","year":"2020","journal-title":"Curr. Treat. Options Infect. Dis."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"S3","DOI":"10.1097\/INF.0000000000003318","article-title":"Comparison of Antimicrobial Stewardship and Infection Prevention and Control Activities and Resources Between Low-\/Middle-and High-income Countries","volume":"41","author":"Villanueva","year":"2022","journal-title":"Pediatr. Infect. Dis. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s11908-017-0572-y","article-title":"Infection Control in Limited Resources Countries: Challenges and Priorities","volume":"19","year":"2017","journal-title":"Curr. Infect. Dis. Rep."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1038\/s41746-022-00700-y","article-title":"Artificial intelligence for strengthening healthcare systems in low-and middle-income countries: A systematic scoping review","volume":"5","author":"Singh","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1590\/0104-07072015002940013","article-title":"Mortality and risks related to healthcare-associated infection","volume":"24","author":"Souza","year":"2015","journal-title":"Texto Contexto-Enferm."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"De Oliveira, D.M., Forde, B.M., Kidd, T.J., Harris, P.N., Schembri, M.A., Beatson, S.A., Paterson, D.L., and Walker, M.J. (2020). Antimicrobial resistance in ESKAPE pathogens. Clin. Microbiol. Rev., 33.","DOI":"10.1128\/CMR.00181-19"},{"key":"ref_85","first-page":"3","article-title":"The clinical importance of emerging ESKAPE pathogens in nosocomial infections","volume":"7","author":"Navidinia","year":"2016","journal-title":"J. Paramed. Sci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1586\/eri.13.12","article-title":"Clinical relevance of the ESKAPE pathogens","volume":"11","author":"Pendleton","year":"2013","journal-title":"Expert. Rev. Anti-Infect. Ther."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Santajit, S., and Indrawattana, N. (2016). Mechanisms of antimicrobial resistance in ESKAPE pathogens. BioMed Res. Int., 2016.","DOI":"10.1155\/2016\/2475067"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis","volume":"399","author":"Murray","year":"2022","journal-title":"Lancet"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"246","DOI":"10.37034\/infeb.v6i1.854","article-title":"Optimizing Machine Learning Models for Urinary Tract Infection Diagnostics: A Comparative Study of Logistic Regression and Random Forest","volume":"6","author":"Airlangga","year":"2024","journal-title":"J. Inform. Ekon. Bisnis"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"8806","DOI":"10.1039\/D2NR01277D","article-title":"Differentiation and classification of bacterial endotoxins based on surface enhanced Raman scattering and advanced machine learning","volume":"14","author":"Yang","year":"2022","journal-title":"Nanoscale"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s44163-023-00049-5","article-title":"Evaluation of artificial intelligence techniques in disease diagnosis and prediction","volume":"3","author":"Kaplanoglu","year":"2023","journal-title":"Discov. Artif. Intell."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3934\/publichealth.2024004","article-title":"Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities","volume":"11","author":"Rahman","year":"2024","journal-title":"AIMS Public Health"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Rahman, A., Islam, M.J., Karim, M.R., Kundu, D., and Kabir, S. (2021, January 14\u201316). An Intelligent Vaccine Distribution Process in COVID-19 Pandemic Through Blockchain-Sdn Framework from Bangladesh Perspective. Proceedings of the 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, Bangladesh.","DOI":"10.1109\/ICECIT54077.2021.9641303"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1007\/s12559-024-10259-3","article-title":"TB-CXRNet: Tuberculosis and Drug-Resistant Tuberculosis Detection Technique Using Chest X-ray Images","volume":"16","author":"Rahman","year":"2024","journal-title":"Cogn. Comput."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"100136","DOI":"10.1016\/j.fraope.2024.100136","article-title":"Optimization of Machine Learning Models through Quantization and Data Bit Reduction in Healthcare Datasets","volume":"8","author":"Goswami","year":"2024","journal-title":"Frankl. Open"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1177\/08404704221125368","article-title":"Equity within AI systems: What can health leaders expect?","volume":"36","author":"Gurevich","year":"2023","journal-title":"Healthc. Manage. Forum."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/bs.mim.2024.05.009","article-title":"Chapter Thirteen\u2014Ethical and social issues related to AI in healthcare","volume":"Volume 55","author":"Srivastava","year":"2024","journal-title":"Methods in Microbiology"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"788","DOI":"10.4103\/idoj.idoj_543_23","article-title":"Data Privacy in Healthcare: In the Era of Artificial Intelligence","volume":"14","author":"Yadav","year":"2023","journal-title":"Indian. Dermatol. Online J."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., and Qadir, J. (2023). Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput. Biol. Med., 158.","DOI":"10.1016\/j.compbiomed.2023.106848"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"e26297","DOI":"10.1016\/j.heliyon.2024.e26297","article-title":"Ethical and regulatory challenges of AI technologies in healthcare: A narrative review","volume":"10","author":"Mennella","year":"2024","journal-title":"Heliyon"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Bohr, A., and Memarzadeh, K. (2020). Chapter 12\u2014Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, Academic Press.","DOI":"10.1016\/B978-0-12-818438-7.00002-2"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s13054-016-1208-6","article-title":"36th International Symposium on Intensive Care and Emergency Medicine: Brussels, Belgium, 15\u201318 March 2016","volume":"20","author":"Bateman","year":"2016","journal-title":"Crit Care"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Alie, M.S., and Negesse, Y. (2024). Machine learning prediction of adolescent HIV testing services in Ethiopia. Front. Public Health, 12.","DOI":"10.3389\/fpubh.2024.1341279"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1016\/j.ajic.2007.06.002","article-title":"Economics of infection control surveillance technology: Cost-effective or just cost?","volume":"36","author":"Furuno","year":"2008","journal-title":"Am. J. Infect. Control"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13756-024-01428-y","article-title":"Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data","volume":"13","author":"Li","year":"2024","journal-title":"Antimicrob. Resist. Infect. Control"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1422","DOI":"10.1057\/s41599-024-03947-w","article-title":"Artificial intelligence for low income countries","volume":"11","author":"Khan","year":"2024","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1080\/1364557032000119616","article-title":"Scoping studies: Towards a methodological framework","volume":"8","author":"Arksey","year":"2005","journal-title":"Int. J. Social. Res. Methodol."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/1748-5908-5-69","article-title":"Scoping studies: Advancing the methodology","volume":"5","author":"Levac","year":"2010","journal-title":"Implement. Sci."},{"key":"ref_109","unstructured":"Bryman, A.B.B. (1994). Qualitative Data Analysis for Applied Policy Research. Analyzing Qualitative Data, Routledge Member of the Taylor and Francis Group."},{"key":"ref_110","unstructured":"Ritchie, J.L.J. (2003). Analysis: Practices, Principles and Processes. Qualitative Research Practice, SAGE."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jclinepi.2016.01.021","article-title":"PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement","volume":"75","author":"McGowan","year":"2016","journal-title":"J. Clin. Epidemiol."},{"key":"ref_112","unstructured":"Blackwood, D. (2015). Peer Review of Electronic Search Strategies (PRESS): \u201cCan you check my systematic review search strategy?\u201d. HLA News, 9\u201310."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_114","first-page":"203","article-title":"Open science framework (OSF)","volume":"105","author":"Foster","year":"2017","journal-title":"J. Med. Libr. Assoc. JMLA"}],"container-title":["Antibiotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6382\/14\/3\/256\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:45:58Z","timestamp":1760028358000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6382\/14\/3\/256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,2]]},"references-count":114,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["antibiotics14030256"],"URL":"https:\/\/doi.org\/10.3390\/antibiotics14030256","relation":{},"ISSN":["2079-6382"],"issn-type":[{"value":"2079-6382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,2]]}}}