All Issue

2025 Vol.6, Issue 4

Review article

31 December 2025. pp. 147-158
Abstract
Sorry, not available.
Click the PDF button.
References
1

WHO (World Health Organization), A Financial Model for an Impact Investment Fund for the Development of Antibacterial Treatments and Diagnostics: A User Guide. 1st ed. Geneva: World Health Organization; 2020. 1 p.

2

Liu GY et al., Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res, 2024

10.1186/s40779-024-00510-138254241PMC10804841
3

Pendleton JN et al., Clinical relevance of the ESKAPE pathogens, Expert Rev Anti Infect Ther, 2013

10.1586/eri.13.12
4

Branda F et al., Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future, Antibiotics (Basel), 2024

10.3390/antibiotics1306050238927169PMC11200959
5

Hu F et al., CHINET efforts to control antimicrobial resistance in China, J Glob Antimicrob Resist, 2020

10.1016/j.jgar.2020.03.007
6

WHO releases report on state of development of antibacterials [Internet]. Available at: https://www.who.int/news/item/14-06-2024-who-releases-report-on-state-of-development-of-antibacterials

7

2023 Antibacterial agents in clinical and preclinical development: an overview and analysis [Internet]. Available at: https://www.who.int/publications/i/item/9789240094000

8

Terreni M et al., New Antibiotics for Multidrug-Resistant Bacterial Strains: Latest Research Developments and Future Perspectives, Molecules, 2021

10.3390/molecules2609267134063264PMC8125338
9

Bartlett JG et al., Seven ways to preserve the miracle of antibiotics, Clin Infect Dis, 2013

10.1093/cid/cit070
10

de la Lastra JMP et al., From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review, J Med Syst, 2024

10.1007/s10916-024-02089-539088151PMC11294375
11

Weis C et al., Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning, Nat Med, 2022

10.1038/s41591-021-01619-9
12

Kanjilal S et al., A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection, Sci Transl Med, 2020

10.1126/scitranslmed.aay506733148625PMC9527766
13

Ray KN et al., Antibiotic Prescribing for Acute Respiratory Tract Infections During Telemedicine Visits Within a Pediatric Primary Care Network, Acad Pediatr, 2021

10.1016/j.acap.2021.03.008
14

Khaledi A et al., Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics, EMBO Mol Med, 2020

15

Wang S et al., A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Staphylococcus aureus Through Machine Learning Analysis of Genome Data, Front Microbiol, 2022

10.3389/fmicb.2022.84128935308374PMC8924536
16

Weis C et al., Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning, Nat Med, 2022

10.1038/s41591-021-01619-9
17

Pinto-de-Sá R et al., Brave New World of Artificial Intelligence: Its Use in Antimicrobial Stewardship-A Systematic Review, Antibiotics (Basel), 2024

10.3390/antibiotics1304030738666983PMC11047419
18

Melo MCR et al., Accelerating antibiotic discovery through artificial intelligence, Commun Biol, 2021

10.1038/s42003-021-02586-034504303PMC8429579
19

Humphries R et al., Effective implementation of the Accelerate Pheno™ system for positive blood cultures, J Antimicrob Chemother, 2019

10.1093/jac/dky53430690541PMC6382030
20

Zalas-Więcek P et al., The Accelerate Pheno™ System-A New Tool in Microbiological Diagnostics of Bloodstream Infections: A Pilot Study from Poland, Pathogens, 2022

10.3390/pathogens1112141536558749PMC9781321
21

Morecchiato F et al., Evaluation of Quantamatrix dRASTTM system for rapid antimicrobial susceptibility testing of bacterial isolates from positive blood cultures, in comparison with commercial Micronaut broth microdilution system, Diagn Microbiol Infect Dis, 2024

10.1016/j.diagmicrobio.2024.116436
22

Weis C et al., Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning, Nat Med, 2022

10.1038/s41591-021-01619-9
23

Khaledi A et al., Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics, EMBO Mol Med, 2020

24

Wang S et al., A Practical Approach for Predicting Antimicrobial Phenotype Resistance in Staphylococcus aureus Through Machine Learning Analysis of Genome Data, Front Microbiol, 2022

10.3389/fmicb.2022.84128935308374PMC8924536
25

Morecchiato F et al., Evaluation of Quantamatrix dRASTTM system for rapid antimicrobial susceptibility testing of bacterial isolates from positive blood cultures, in comparison with commercial Micronaut broth microdilution system, Diagn Microbiol Infect Dis, 2024

10.1016/j.diagmicrobio.2024.116436
26

Kim TY et al., Evaluation of the QMAC-dRAST System Version 2.5 for Rapid Antimicrobial Susceptibility Testing of Gram-Negative Bacteria From Positive Blood Culture Broth and Subcultured Colony Isolates, J Clin Lab Anal, 2024

10.1002/jcla.2504338804639PMC11137843
27

Yang J et al., Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections, NPJ Antimicrob Resist, 2023

10.1101/2023.01.09.23284299
28

Castellanos LR et al., A novel machine-learning aided platform for rapid detection of urine ESBLs and carbapenemases: URECA-LAMP, J Clin Microbiol, 2024

10.1128/jcm.00869-2439445836PMC11559160
29

Ardila CM et al., Integrating whole genome sequencing and machine learning for predicting antimicrobial resistance in critical pathogens: a systematic review of antimicrobial susceptibility tests, PeerJ, 2024

10.7717/peerj.1821339399439PMC11470768
30

Yelin I et al., Personal clinical history predicts antibiotic resistance of urinary tract infections, Nat Med, 2019

10.1101/384842
31

Yang J et al., Interpretable machine learning-based decision support for prediction of antibiotic resistance for complicated urinary tract infections, NPJ Antimicrob Resist, 2023

10.1101/2023.01.09.23284299
32

Corbin CK et al., Personalized antibiograms for machine learning driven antibiotic selection, Commun Med (Lond), 2022

10.1038/s43856-022-00094-835603264PMC9053259
33

Bolton WJ et al., Machine learning and synthetic outcome estimation for individualised antimicrobial cessation, Front Digit Health, 2022

10.3389/fdgth.2022.99721936479189PMC9719971
34

Kanjilal S et al., A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection, Sci Transl Med, 2020

10.1126/scitranslmed.aay506733148625PMC9527766
35

Gohil SK et al., Stewardship Prompts to Improve Antibiotic Selection for Urinary Tract Infection: The INSPIRE Randomized Clinical Trial, JAMA, 2024

10.1001/jama.2024.625938639723PMC11185978
36

Gohil SK et al., Stewardship Prompts to Improve Antibiotic Selection for Pneumonia: The INSPIRE Randomized Clinical Trial, JAMA, 2024

10.1001/jama.2024.624838639729PMC11185977
37

Goodman KE et al., Real-world Antimicrobial Stewardship Experience in a Large Academic Medical Center: Using Statistical and Machine Learning Approaches to Identify Intervention “Hotspots” in an Antibiotic Audit and Feedback Program, Open Forum Infect Dis, 2022

10.1093/ofid/ofac28935873287PMC9297307
38

Bolton WJ et al., Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning, Nat Commun, 2024

10.1038/s41467-024-44740-238218885PMC10787786
39

Pennisi F et al., Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy, Eur J Clin Microbiol Infect Dis, 2025

10.1007/s10096-024-05027-y
40

Corbin CK et al., Personalized antibiograms for machine learning driven antibiotic selection, Commun Med (Lond), 2022

10.1038/s43856-022-00094-835603264PMC9053259
41

Dolatkhah Laein G et al., Telemedicine interventions for improving antibiotic stewardship and prescribing: A systematic review, PLoS One, 2025

10.1371/journal.pone.032084040179108PMC11967954
42

Stokes JM et al., A Deep Learning Approach to Antibiotic Discovery, Cell, 2020

43

Wan F et al., Deep-learning-enabled antibiotic discovery through molecular de-extinction, Nat Biomed Eng, 2024

10.1038/s41551-024-01201-x38862735PMC11310081
Information
  • Publisher :The Association of Korean Urologists
  • Publisher(Ko) :대한비뇨의학과의사회
  • Journal Title :비뇨의학 Urology Digest
  • Volume : 6
  • No :4
  • Pages :147-158