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From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.

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Τίτλος: From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.
Συγγραφείς: Rabbani SA; RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates., El-Tanani M; RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates., Matalka II; RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates., Sharma S; Amity Institute of Pharmacy, Amity University, Gurugram, Haryana, India., Saini M; Amity Institute of Pharmacy, Amity University, Gurugram, Haryana, India.; Geeta Institute of Pharmacy, Geeta University, Panipat, Haryana, India., Kumar R; Amity Institute of Pharmacy, Amity University, Gurugram, Haryana, India.; Department of Pharmacy, Jagannath University, Bahadurgarh, Haryana, India.
Πηγή: Frontiers in public health [Front Public Health] 2026 Sep 17; Vol. 14, pp. 1922265. Date of Electronic Publication: 2026 Sep 17 (Print Publication: 2026).
Τύπος έκδοσης: Journal Article; Scoping Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Frontiers Editorial Office Country of Publication: Switzerland NLM ID: 101616579 Publication Model: eCollection Cited Medium: Internet ISSN: 2296-2565 (Electronic) Linking ISSN: 22962565 NLM ISO Abbreviation: Front Public Health Subsets: MEDLINE
Imprint Name(s): Original Publication: Lausanne : Frontiers Editorial Office
Ιατρικοί όροι (MeSH): Population Surveillance*/methods , Drug Resistance, Bacterial* , Machine Learning* , One Health*, Animals ; Humans ; Predictive Learning Models
Περίληψη: Background: Antimicrobial resistance (AMR) is a leading global health threat requiring coordinated surveillance across human, animal, environmental, and genomic systems. Machine learning is increasingly applied to AMR data, yet its contribution to actionable surveillance intelligence, rather than prediction alone, remains poorly defined.
Objective: To map how machine-learning approaches generate AMR surveillance intelligence, to characterise their validation and implementation maturity, and to propose a framework distinguishing technical prediction from actionable surveillance intelligence.
Methods: We conducted a scoping review following JBI methodology and PRISMA-ScR reporting. PubMed/MEDLINE, Scopus, and Web of Science were searched from January 2015 to May 2026 for studies applying machine learning or related methods to AMR surveillance intelligence. Two reviewers independently screened and charted records. Of 1,985 records, 66 met eligibility and formed the working evidence base; 41 studies (40 core empirical and one supporting preprint) were appraised against TRIPOD+AI- and PROBAST-aligned reporting, validation, and implementation-readiness domains.
Results: Machine learning was applied across five clusters: clinical and electronic-health-record risk prediction and decision support; genomic and whole-genome-sequencing prediction; MALDI-TOF-based rapid resistance prediction; wastewater and metagenomic surveillance; and environmental, animal, food-chain, and One Health early warning. Prediction and risk stratification predominated, but validation maturity was limited: most studies were retrospective or internally validated, with few using external, cross-country, temporal, prospective, or drift-focused evaluation. On appraisal, discrimination was reported in 31 of 41 studies (76%) and explainability in 26 (63%); by contrast, external or temporal validation was present in only 15 (37%), calibration in 5 (12%), prospective evaluation in 1 (2%), and operational deployment with measured clinical or public-health impact in a single study (2%).
Conclusion: Machine learning can support AMR surveillance intelligence across clinical, genomic, diagnostic, environmental, and One Health settings, but the evidence demonstrates technical feasibility far more convincingly than operational readiness. Realising this transition will require external and prospective validation, calibration and drift monitoring, transparent and equitable reporting, workflow integration, and explicit linkage of model outputs to clinical and public-health action. We propose a One Health AMR Surveillance Intelligence Framework to organise this shift from data generation toward actionable, adaptive surveillance intelligence.
(Copyright © 2026 Rabbani, El-Tanani, Matalka, Sharma, Saini and Kumar.)
Competing Interests: The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Lancet. 2024 Sep 28;404(10459):1199-1226. (PMID: 39299261)
Expert Rev Anti Infect Ther. 2024 Oct;22(10):819-833. (PMID: 39155449)
PLOS Digit Health. 2024 Oct 16;3(10):e0000641. (PMID: 39413052)
Int J Mol Sci. 2025 Jan 28;26(3):. (PMID: 39940908)
PLoS One. 2025 Feb 25;20(2):e0319460. (PMID: 39999193)
Front Microbiol. 2026 Feb 11;16:1722229. (PMID: 41757357)
Vet Sci. 2025 Nov 12;12(11):. (PMID: 41295718)
Ann Intern Med. 2018 Oct 2;169(7):467-473. (PMID: 30178033)
Front Microbiol. 2023 May 11;14:1160224. (PMID: 37250043)
Sci Rep. 2025 Aug 20;15(1):30509. (PMID: 40835633)
Microbiome. 2025 Jan 7;13(1):3. (PMID: 39763003)
Wellcome Open Res. 2018 Oct 10;3:131. (PMID: 30756093)
Sci Transl Med. 2020 Nov 4;12(568):. (PMID: 33148625)
Nat Med. 2022 May;28(5):924-933. (PMID: 35585198)
J Clin Microbiol. 2021 Jun 18;59(7):e0126020. (PMID: 33536291)
NPJ Antimicrob Resist. 2025 Mar 28;3(1):21. (PMID: 40155701)
Front Microbiol. 2022 Feb 18;12:804484. (PMID: 35250909)
Sci Rep. 2024 Sep 30;14(1):22683. (PMID: 39349551)
Ann Intern Med. 2019 Jan 1;170(1):51-58. (PMID: 30596875)
Digit Health. 2022 Oct 27;8:20552076221134455. (PMID: 36325438)
Int J Antimicrob Agents. 2022 Nov-Dec;60(5-6):106684. (PMID: 36279973)
Environ Pollut. 2025 Oct 15;383:126865. (PMID: 40701495)
BMC Genomics. 2024 Mar 18;25(1):287. (PMID: 38500034)
Bioinformatics. 2024 Oct 1;40(10):. (PMID: 39254573)
Lancet. 2022 Feb 12;399(10325):629-655. (PMID: 35065702)
Gigascience. 2026 Jan 21;15:. (PMID: 41553357)
BMJ. 2024 Apr 16;385:e078378. (PMID: 38626948)
Nat Commun. 2022 Mar 23;13(1):1553. (PMID: 35322038)
J Med Internet Res. 2020 Feb 20;22(2):e16866. (PMID: 32130134)
Clin Infect Dis. 2021 Jun 1;72(11):e848-e855. (PMID: 33070171)
mSystems. 2025 Jul 22;10(7):e0058425. (PMID: 40503893)
Water Res. 2024 Sep 15;262:121989. (PMID: 39018584)
Nat Commun. 2019 Mar 8;10(1):1124. (PMID: 30850636)
Sci Rep. 2024 Jul 16;14(1):16464. (PMID: 39013934)
Clin Microbiol Infect. 2025 Aug;31(8):1336-1342. (PMID: 40113208)
J Clin Microbiol. 2025 Dec 17;63(12):e0118625. (PMID: 41296602)
J Clin Microbiol. 2019 Jan 30;57(2):. (PMID: 30333126)
BMJ. 2025 Feb 5;388:e081554. (PMID: 39909534)
Front Microbiol. 2024 Jan 11;14:1320312. (PMID: 38274740)
Nat Commun. 2024 Mar 6;15(1):2036. (PMID: 38448409)
Nat Med. 2022 Jan;28(1):164-174. (PMID: 35013613)
Vet Sci. 2025 Sep 26;12(10):. (PMID: 41150075)
Front Cell Infect Microbiol. 2026 Apr 16;16:1795720. (PMID: 42079745)
BMJ. 2025 Mar 24;388:e082505. (PMID: 40127903)
Nat Commun. 2022 Dec 1;13(1):7251. (PMID: 36456547)
NPJ Digit Med. 2025 Jan 27;8(1):61. (PMID: 39870860)
Int J Antimicrob Agents. 2025 Nov;66(5):107575. (PMID: 40675197)
AMIA Annu Symp Proc. 2025 May 22;2024:857-864. (PMID: 40417584)
Antimicrob Resist Infect Control. 2024 Jul 6;13(1):74. (PMID: 38971777)
Commun Med (Lond). 2024 Oct 10;4(1):197. (PMID: 39390045)
Sci Rep. 2024 Oct 15;14(1):24139. (PMID: 39406803)
Infect Drug Resist. 2025 May 06;18:2255-2269. (PMID: 40353201)
Front Microbiol. 2020 May 25;11:1013. (PMID: 32528441)
Contributed Indexing: Keywords: One Health; antimicrobial resistance; artificial intelligence; early warning; forecasting; genomic surveillance; machine learning; public health decision support
Entry Date(s): Date Created: 20261002 Date Completed: 20261002 Latest Revision: 20261006
Update Code: 20261006
PubMed Central ID: PMC13627374
DOI: 10.3389/fpubh.2026.1922265
PMID: 42824150
Βάση Δεδομένων: MEDLINE
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  Data: From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.
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  Data: <searchLink fieldCode="AU" term="%22Rabbani+SA%22">Rabbani SA</searchLink>; RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.<br /><searchLink fieldCode="AU" term="%22El-Tanani+M%22">El-Tanani M</searchLink>; RAK College of Pharmacy, RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.<br /><searchLink fieldCode="AU" term="%22Matalka+II%22">Matalka II</searchLink>; RAK Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.<br /><searchLink fieldCode="AU" term="%22Sharma+S%22">Sharma S</searchLink>; Amity Institute of Pharmacy, Amity University, Gurugram, Haryana, India.<br /><searchLink fieldCode="AU" term="%22Saini+M%22">Saini M</searchLink>; Amity Institute of Pharmacy, Amity University, Gurugram, Haryana, India.; Geeta Institute of Pharmacy, Geeta University, Panipat, Haryana, India.<br /><searchLink fieldCode="AU" term="%22Kumar+R%22">Kumar R</searchLink>; Amity Institute of Pharmacy, Amity University, Gurugram, Haryana, India.; Department of Pharmacy, Jagannath University, Bahadurgarh, Haryana, India.
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  Data: <searchLink fieldCode="JN" term="%22101616579%22">Frontiers in public health</searchLink> [Front Public Health] 2026 Sep 17; Vol. 14, pp. 1922265. <i>Date of Electronic Publication: </i>2026 Sep 17 (<i>Print Publication: </i>2026).
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  Data: Background: Antimicrobial resistance (AMR) is a leading global health threat requiring coordinated surveillance across human, animal, environmental, and genomic systems. Machine learning is increasingly applied to AMR data, yet its contribution to actionable surveillance intelligence, rather than prediction alone, remains poorly defined.<br />Objective: To map how machine-learning approaches generate AMR surveillance intelligence, to characterise their validation and implementation maturity, and to propose a framework distinguishing technical prediction from actionable surveillance intelligence.<br />Methods: We conducted a scoping review following JBI methodology and PRISMA-ScR reporting. PubMed/MEDLINE, Scopus, and Web of Science were searched from January 2015 to May 2026 for studies applying machine learning or related methods to AMR surveillance intelligence. Two reviewers independently screened and charted records. Of 1,985 records, 66 met eligibility and formed the working evidence base; 41 studies (40 core empirical and one supporting preprint) were appraised against TRIPOD+AI- and PROBAST-aligned reporting, validation, and implementation-readiness domains.<br />Results: Machine learning was applied across five clusters: clinical and electronic-health-record risk prediction and decision support; genomic and whole-genome-sequencing prediction; MALDI-TOF-based rapid resistance prediction; wastewater and metagenomic surveillance; and environmental, animal, food-chain, and One Health early warning. Prediction and risk stratification predominated, but validation maturity was limited: most studies were retrospective or internally validated, with few using external, cross-country, temporal, prospective, or drift-focused evaluation. On appraisal, discrimination was reported in 31 of 41 studies (76%) and explainability in 26 (63%); by contrast, external or temporal validation was present in only 15 (37%), calibration in 5 (12%), prospective evaluation in 1 (2%), and operational deployment with measured clinical or public-health impact in a single study (2%).<br />Conclusion: Machine learning can support AMR surveillance intelligence across clinical, genomic, diagnostic, environmental, and One Health settings, but the evidence demonstrates technical feasibility far more convincingly than operational readiness. Realising this transition will require external and prospective validation, calibration and drift monitoring, transparent and equitable reporting, workflow integration, and explicit linkage of model outputs to clinical and public-health action. We propose a One Health AMR Surveillance Intelligence Framework to organise this shift from data generation toward actionable, adaptive surveillance intelligence.<br /> (Copyright © 2026 Rabbani, El-Tanani, Matalka, Sharma, Saini and Kumar.)
– Name: Abstract
  Label: Competing Interests
  Group: Ab
  Data: The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
– Name: Ref
  Label: References
  Group: RefInfo
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(PMID: <searchLink fieldCode="PM" term="%2241295718%22">41295718)</searchLink><br />Ann Intern Med. 2018 Oct 2;169(7):467-473. (PMID: <searchLink fieldCode="PM" term="%2230178033%22">30178033)</searchLink><br />Front Microbiol. 2023 May 11;14:1160224. (PMID: <searchLink fieldCode="PM" term="%2237250043%22">37250043)</searchLink><br />Sci Rep. 2025 Aug 20;15(1):30509. (PMID: <searchLink fieldCode="PM" term="%2240835633%22">40835633)</searchLink><br />Microbiome. 2025 Jan 7;13(1):3. (PMID: <searchLink fieldCode="PM" term="%2239763003%22">39763003)</searchLink><br />Wellcome Open Res. 2018 Oct 10;3:131. (PMID: <searchLink fieldCode="PM" term="%2230756093%22">30756093)</searchLink><br />Sci Transl Med. 2020 Nov 4;12(568):. (PMID: <searchLink fieldCode="PM" term="%2233148625%22">33148625)</searchLink><br />Nat Med. 2022 May;28(5):924-933. (PMID: <searchLink fieldCode="PM" term="%2235585198%22">35585198)</searchLink><br />J Clin Microbiol. 2021 Jun 18;59(7):e0126020. 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      – TitleFull: From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.
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      – PersonEntity:
          Name:
            NameFull: El-Tanani M
      – PersonEntity:
          Name:
            NameFull: Matalka II
      – PersonEntity:
          Name:
            NameFull: Sharma S
      – PersonEntity:
          Name:
            NameFull: Saini M
      – PersonEntity:
          Name:
            NameFull: Kumar R
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 17
              M: 09
              Text: 2026 Sep 17
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 2296-2565
          Numbering:
            – Type: volume
              Value: 14
          Titles:
            – TitleFull: Frontiers in public health
              Type: main
ResultId 1