Academic Journal
From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health.
| Τίτλος: | From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health. |
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| 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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| 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 |
| ISSN: | 2296-2565 |
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| DOI: | 10.3389/fpubh.2026.1922265 |