ARTIFICIAL INTELLIGENCE IN ANTIMICROBIAL STEWARDSHIP: CLINICAL APPLICATIONS, PREDICTIVE PERFORMANCE, AND IMPLEMENTATION CHALLENGES
DOI:
https://doi.org/10.31435/ijitss.1(49).2026.5272Keywords:
Artificial Intelligence, Antimicrobial Stewardship, Antimicrobial Resistance, Machine Learning, Clinical Decision Support Systems, Antibiotic PrescribingAbstract
Artificial intelligence (AI) and machine learning (ML) are increasingly being proposed as tools to strengthen antimicrobial stewardship (AMS), particularly where antibiotic decisions depend on large volumes of clinical, microbiological, and prescribing data. This review synthesizes recent PubMed-indexed full-text evidence on AI in AMS, with emphasis on clinical application, predictive performance, implementation readiness, and governance. A structured narrative review was conducted using a targeted PubMed search updated on 8 March 2026. English-language peer-reviewed full-text papers published from 2017 through early 2026 were eligible when they addressed antimicrobial prescribing, susceptibility or resistance prediction, rapid diagnostic support, stewardship workflow, implementation, or ethical and organizational aspects of AI-enabled prescribing. Thirty-four papers were retained for structured synthesis, and two additional papers were used for contextual framing. The evidence indicates that AI is most mature in bounded stewardship tasks, especially individualized empiric prescribing, resistance prediction, rapid diagnostic interpretation, and prioritization of stewardship interventions. Newer open-access studies also show movement toward reusable stewardship datasets, real-time triage tools, and models for high-risk populations. However, the field remains dominated by retrospective, single-centre, and syndrome-specific studies, while external validation, workflow evaluation, and post-deployment governance are still limited. Current evidence therefore supports AI as a useful adjunct to stewardship teams rather than an autonomous replacement for clinical judgment.
References
Al Mazrouei, N., Elnour, A. A., Badi, S., Alsulami, F. T., Saeed, A. A. M., Al-Kubaisi, K. A., Menon, V., Khidir, I. Y., Ismail, M., Mahagoub, M. M. O., Ramisetti, A. K., & Elkarib, R. (2025). The impact of artificial intelligence on the prescribing, selection, resistance, and stewardship of antimicrobials: A scoping review. BMC Infectious Diseases, 26, 222. https://doi.org/10.1186/s12879-025-12320-4
AlGain, S., Marra, A. R., Kobayashi, T., Marra, P. S., Celeghini, P. D., Hsieh, M. K., Shatari, M. A., Althagafi, S., Alayed, M., Ranavaya, J. I., Boodhoo, N. A., Meade, N. O., Fu, D., Sampson, M. M., Rodriguez-Nava, G., Zimmet, A. N., Ha, D., Alsuhaibani, M., Huddleston, B. S., & Salinas, J. L. (2025). Can we rely on artificial intelligence to guide antimicrobial therapy? A systematic literature review. Antimicrobial Stewardship & Healthcare Epidemiology, 5(1), e90. https://doi.org/10.1017/ash.2025.47
Blechman, S. E., & Wright, E. S. (2024). Applications of machine learning on electronic health record data to combat antibiotic resistance. The Journal of Infectious Diseases, 230(5), 1073–1082. https://doi.org/10.1093/infdis/jiae348
Bolton, W. J., Wilson, R., Gilchrist, M., Georgiou, P., Holmes, A., & Rawson, T. M. (2024). Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning. Nature Communications, 15(1), 506. https://doi.org/10.1038/s41467-024-44740-2
Bolton, W. J., Wilson, R., Gilchrist, M., Georgiou, P., Holmes, A., & Rawson, T. M. (2025). The impact of artificial intelligence-driven decision support on uncertain antimicrobial prescribing: A randomised, multimethod study. The Lancet Digital Health, 7(11), 100912. https://doi.org/10.1016/j.landig.2025.100912
Bosetti, D., Grant, R., & Catho, G. (2025). Computerized decision support for antimicrobial prescribing: What every antibiotic steward should know. Antimicrobial Stewardship & Healthcare Epidemiology, 5(1), e210. https://doi.org/10.1017/ash.2025.10091
Bystritsky, R. J., Beltran, A., Young, A. T., Wong, A., Hu, X., & Doernberg, S. B. (2020). Machine learning for the prediction of antimicrobial stewardship intervention in hospitalized patients receiving broad-spectrum agents. Infection Control & Hospital Epidemiology, 41(9), 1022–1027. https://doi.org/10.1017/ice.2020.213
Cai, T., Anceschi, U., Prata, F., Collini, L., Brugnolli, A., Migno, S., Rizzo, M., Liguori, G., Gallelli, L., Wagenlehner, F. M. E., Bjerklund Johansen, T. E., Montanari, L., Palmieri, A., & Tascini, C. (2023). Artificial intelligence can guide antibiotic choice in recurrent UTIs and become an important aid to improve antimicrobial stewardship. Antibiotics, 12(2), 375. https://doi.org/10.3390/antibiotics12020375
Cavallaro, M., Moran, E., Collyer, B., McCarthy, N. D., Green, C., & Keeling, M. J. (2023). Informing antimicrobial stewardship with explainable AI. PLOS Digital Health, 2(1), e0000162. https://doi.org/10.1371/journal.pdig.0000162
Cesaro, A., Hoffman, S. C., Das, P., & de la Fuente-Nunez, C. (2025). Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance. npj Antimicrobials and Resistance, 3(1), 2. https://doi.org/10.1038/s44259-024-00068-x
Chang, A., & Chen, J. H. (2022). BSAC Vanguard Series: Artificial intelligence and antibiotic stewardship. Journal of Antimicrobial Chemotherapy, 77(5), 1216–1217. https://doi.org/10.1093/jac/dkac096
Dutey-Magni, P. F., Brown, M., Harris, S., Curtis, C., Dobson, R., Chowdhury, H., Cawthorn, A., De, S., Stone, N., Cooper, J., & Shallcross, L. (2026). Supervised machine learning to identify hospital inpatients needing a change of antibiotic therapy in real time: Preclinical diagnostic evaluation and feasibility study. Open Forum Infectious Diseases, 13(1), ofaf721. https://doi.org/10.1093/ofid/ofaf721
Dyar, O. J., Huttner, B., Schouten, J., Pulcini, C., & ESGAP (ESCMID Study Group for Antimicrobial Stewardship). (2017). What is antimicrobial stewardship? Clinical Microbiology and Infection, 23(11), 793–798. https://doi.org/10.1016/j.cmi.2017.08.026
El Arab, R. A., Almoosa, Z., Alkhunaizi, M., Abuadas, F. H., & Somerville, J. (2025). Artificial intelligence in hospital infection prevention: An integrative review. Frontiers in Public Health, 13, 1547450. https://doi.org/10.3389/fpubh.2025.1547450
Gallardo-Pizarro, A., Teijón-Lumbreras, C., Aiello, T. F., Monzó-Gallo, P., Martinez-Urrea, A., Terrones-Campos, C., Cuervo, G., Morata, L., Castro, P., Casals, C., Espasa, M., Esteve, J., Nicolas, J. M., Mensa, J., Soriano, A., & Garcia-Vidal, C. (2026). Towards personalised empirical antibiotic therapy in febrile neutropenia: A theoretical model based on machine learning and prior colonisation with multidrug-resistant gram-negative bacilli—A retrospective proof-of-concept cohort study. Therapeutic Advances in Infectious Disease, 13, 20499361251411061. https://doi.org/10.1177/20499361251411061
GBD 2021 Antimicrobial Resistance Collaborators. (2024). Global burden of bacterial antimicrobial resistance 1990–2021: A systematic analysis with forecasts to 2050. The Lancet, 404(10459), 1199–1226. https://doi.org/10.1016/S0140-6736(24)01867-1
Giacobbe, D. R., Guastavino, S., Marelli, C., Murgia, Y., Mora, S., Signori, A., Rosso, N., Giacomini, M., Campi, C., Piana, M., & Bassetti, M. (2025). Antibiotics and artificial intelligence: Clinical considerations on a rapidly evolving landscape. Infectious Diseases and Therapy, 14(3), 493–500. https://doi.org/10.1007/s40121-025-01114-5
Harandi, H., Shafaati, M., Salehi, M., Roozbahani, M. M., Mohammadi, K., Akbarpour, S., Rahimnia, R., Hassanpour, G., Rahmani, Y., & Seifi, A. (2025). Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review. Artificial Intelligence in Medicine, 162, 103089. https://doi.org/10.1016/j.artmed.2025.103089
Haredasht, F. N., Maddali, M. V., Ma, S. P., Chang, A., Kim, G. Y. E., Banaei, N., Deresinski, S., Goldstein, M. K., Asch, S. M., & Chen, J. H. (2025). Enhancing antibiotic stewardship: A machine learning approach to predicting antibiotic resistance in inpatient care. AMIA Annual Symposium Proceedings, 2024, 857–864.
Hebert, C., Gao, Y., Rahman, P., Dewart, C., Lustberg, M., Pancholi, P., Stevenson, K., Shah, N. S., & Hade, E. M. (2020). Prediction of antibiotic susceptibility for urinary tract infection in a hospital setting. Antimicrobial Agents and Chemotherapy, 64(7), e02236-19. https://doi.org/10.1128/AAC.02236-19
Huang, Z., Lim, H. Y.-F., Ow, J. T., Sun, S. H.-L., & Chow, A. (2024). Doctors’ perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore. Frontiers in Public Health, 12, 1420032. https://doi.org/10.3389/fpubh.2024.1420032
Kanjilal, S., Oberst, M., Boominathan, S., Zhou, H., Hooper, D. C., & Sontag, D. (2020). A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection. Science Translational Medicine, 12(568), eaay5067. https://doi.org/10.1126/scitranslmed.aay5067
Lin, T.-H., Chung, H.-Y., Jian, M.-J., Chang, C.-K., Lin, H.-H., Yu, C.-M., Perng, C.-L., Chang, F.-Y., Chen, C.-W., Chiu, C.-H., & Shang, H.-S. (2024). Artificial intelligence-clinical decision support system for enhanced infectious disease management: Accelerating ceftazidime-avibactam resistance detection in Klebsiella pneumoniae. Journal of Infection and Public Health, 17(10), 102541. https://doi.org/10.1016/j.jiph.2024.102541
Liu, G.-Y., Yu, D., Fan, M.-M., Zhang, X., Jin, Z.-Y., Tang, C., & Liu, X.-F. (2024). Antimicrobial resistance crisis: Could artificial intelligence be the solution? Military Medical Research, 11(1), 7. https://doi.org/10.1186/s40779-024-00510-1
Nateghi Haredasht, F., Amrollahi, F., Maddali, M. V., Marshall, N., Ma, S. P., Cooper, L. N., Johnson, A. O., Wei, Z., Medford, R. J., Kanjilal, S., Banaei, N., Deresinski, S., Goldstein, M. K., Asch, S. M., Chang, A., & Chen, J. H. (2025). Antibiotic resistance microbiology dataset (ARMD): A resource for antimicrobial resistance from EHRs. Scientific Data, 12(1), 1299. https://doi.org/10.1038/s41597-025-05649-7
Panda, P. K., & Ghosh, S. (2025). Ethical use of AI in infectious diagnostic decision and therapeutic stewardship. IDCases, 42, e02356. https://doi.org/10.1016/j.idcr.2025.e02356
Peiffer-Smadja, N., Rawson, T. M., Ahmad, R., Buchard, A., Georgiou, P., Lescure, F.-X., Birgand, G., & Holmes, A. H. (2020). Machine learning for clinical decision support in infectious diseases: A narrative review of current applications. Clinical Microbiology and Infection, 26(5), 584–595. https://doi.org/10.1016/j.cmi.2019.09.009
Pennisi, F., Pinto, A., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025). Artificial intelligence in antimicrobial stewardship: A systematic review and meta-analysis of predictive performance and diagnostic accuracy. European Journal of Clinical Microbiology & Infectious Diseases, 44(3), 463–513. https://doi.org/10.1007/s10096-024-05027-y
Pinto, A., Pennisi, F., Ricciardi, G. E., Signorelli, C., & Gianfredi, V. (2025). Evaluating the impact of artificial intelligence in antimicrobial stewardship: A comparative meta-analysis with traditional risk scoring systems. Infectious Diseases Now, 55(5), 105090. https://doi.org/10.1016/j.idnow.2025.105090
Pinto-de-Sá, R., Sousa-Pinto, B., & Costa-de-Oliveira, S. (2024). Brave new world of artificial intelligence: Its use in antimicrobial stewardship—A systematic review. Antibiotics, 13(4), 307. https://doi.org/10.3390/antibiotics13040307
Rawson, T. M., Zhu, N., Galiwango, R., Cocker, D., Islam, M. S., Myall, A., Vasikasin, V., Wilson, R., Shafiq, N., Das, S., & Holmes, A. H. (2024). Using digital health technologies to optimise antimicrobial use globally. The Lancet Digital Health, 6(12), e914–e925. https://doi.org/10.1016/S2589-7500(24)00198-5
Theodosiou, A. A., & Read, R. C. (2023). Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician. Journal of Infection, 87(4), 287–294. https://doi.org/10.1016/j.jinf.2023.07.006
Tokgöz, P., Hafner, J., & Dockweiler, C. (2023). Factors influencing the implementation of decision support systems for antibiotic prescription in hospitals: A systematic review. BMC Medical Informatics and Decision Making, 23(1), 27. https://doi.org/10.1186/s12911-023-02124-4
Tokgöz, P., Krayter, S., Hafner, J., & Dockweiler, C. (2024). Decision support systems for antibiotic prescription in hospitals: A survey with hospital managers on factors for implementation. BMC Medical Informatics and Decision Making, 24(1), 96. https://doi.org/10.1186/s12911-024-02490-7
Tran-The, T., Heo, E., Lim, S., Suh, Y., Heo, K.-N., Lee, E. E., Lee, H.-Y., Kim, E. S., Lee, J.-Y., & Jung, S. Y. (2024). Development of machine learning algorithms for scaling-up antibiotic stewardship. International Journal of Medical Informatics, 181, 105300. https://doi.org/10.1016/j.ijmedinf.2023.105300
Van Dort, B. A., Penm, J., Ritchie, A., & Baysari, M. T. (2022). The impact of digital interventions on antimicrobial stewardship in hospitals: A qualitative synthesis of systematic reviews. Journal of Antimicrobial Chemotherapy, 77(7), 1828–1837. https://doi.org/10.1093/jac/dkac112
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Copyright (c) 2026 Oliwer Műller, Michał Babicz, Kamil Chudzicki, Wiktor Czyżewski, Daria Danielczyk, Natalia Malatyńska, Anna Szot, Dominik Szydełko, Martyna Szymczyk, Paweł Żurek
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