Antimicrobial Resistance and AI-Based Strategies for Rapid Pathogen Detection

Year : 2026 | Volume : 03 | Issue : 02 | Page : 26 36
    By

    Rohit Saroha,

  • Karan Singh Gehlot,

  • Gauri Mudgal,

  • Prabha Kumari,

  1. Assistant Professor, Department of Physiology Venkateshwara Institute of Medical Sciences, Gajraula, Amroha, Uttar Pradesh, India
  2. Senior Resident, Department of Orthopaedics, ESIC Medical College and Hospital, Faridabad, Haryana, India
  3. PhD Scholar and Assistant Professor, Department of Physiology Divya Jyoti College of Dental Sciences and Research, Modinagar, Uttar Pradesh, India
  4. PhD Scholar and Assistant Professor, , Department of Physiology ITS Dental College, Greater Noida, Uttar Pradesh, India

Abstract

Antimicrobial resistance (AMR) has become a major global health threat, significantly reducing the effectiveness of antimicrobial therapies and increasing the burden of infectious diseases worldwide. The rapid emergence of multidrug-resistant pathogens has created an urgent need for faster, more accurate, and scalable diagnostic approaches to support timely treatment and effective infection control. Artificial intelligence (AI) has emerged as a promising technology capable of transforming pathogen detection and AMR surveillance through the analysis of complex genomic, metagenomic, microbiological, imaging, and clinical datasets. This review explores the role of AI-based strategies in rapid pathogen detection and antimicrobial resistance management. Machine learning, deep learning, natural language processing, and big data analytics are increasingly being utilized to identify pathogens, predict antimicrobial susceptibility, detect resistance genes, and support evidence-based clinical decision-making. Significant applications have been demonstrated in hospital diagnostics, outbreak surveillance, antimicrobial stewardship programs, and precision infectious disease management. Recent advances in AI-assisted diagnostics, biosensor technologies, metagenomic analysis, real-time surveillance systems, and explainable AI have further enhanced the speed, accuracy, and efficiency of infectious disease detection. However, challenges related to data quality, algorithm transparency, regulatory frameworks, clinical implementation, and data privacy remain important considerations for widespread adoption. Future developments involving explainable AI, multi-omics integration, predictive analytics, and precision microbiology are expected to strengthen global efforts against antimicrobial resistance. Overall, AI-driven pathogen detection represents a transformative approach for improving diagnostic accuracy, optimizing antimicrobial therapy, enhancing surveillance capabilities, and supporting the development of more effective strategies to combat antimicrobial resistance

Keywords: Antimicrobial resistance, artificial intelligence, pathogen detection, machine learning, precision microbiology

[This article belongs to Recent Trends in Infectious Diseases ]

How to cite this article:
Rohit Saroha, Karan Singh Gehlot, Gauri Mudgal, Prabha Kumari. Antimicrobial Resistance and AI-Based Strategies for Rapid Pathogen Detection. Recent Trends in Infectious Diseases. 2026; 03(02):26-36.
How to cite this URL:
Rohit Saroha, Karan Singh Gehlot, Gauri Mudgal, Prabha Kumari. Antimicrobial Resistance and AI-Based Strategies for Rapid Pathogen Detection. Recent Trends in Infectious Diseases. 2026; 03(02):26-36. Available from: https://journals.stmjournals.com/rtid/article=2026/view=247561


References

  1. Zubair A, Alkahtani AM, Shahani MY, Afghan N. Gut microbiome-based strategies for HIV prevention and therapy, current challenges and future prospects. Gut Pathog. 2026;18(1):11.
  2. Arnold A, McLellan S, Stokes JM. How AI can help us beat AMR. npj Antimicrob Resist. 2025;3(1):18.
  3. Wong F, de la Fuente-Nunez C, Collins JJ. Leveraging artificial intelligence in the fight against infectious diseases. Science. 2023;381(6654):164–170.
  4. Natto HA, Mahmood AA, Vasanthi RK, Singh DN. Artificial intelligence in combating antimicrobial resistance. Arch Razi Inst. 2025;80(3):605.
  5. Ayesiga I, Yeboah MO, Gmanyami JM, Ovye A, Bulus NG, Nalwanga LE, et al. The application of artificial intelligence in surveillance and control for antimicrobial resistance in hospital-acquired infections. Discov Public Health. 2026;23(1):533.
  6. Li Y, Cui X, Yang X, Liu G, Zhang J. Artificial intelligence in predicting pathogenic microorganisms’ antimicrobial resistance: Challenges, progress, and prospects. Front Cell Infect Microbiol. 2024;14:1482186.
  7. Ho CS, Wong CT, Aung TT, Lakshminarayanan R, Mehta JS, Rauz S, et al. Antimicrobial resistance: A concise update. Lancet Microbe. 2025;6(1).
  8. Mohammed AM, Oleiwi JK, Osman AF, Adam T, Betar BO, Gopinath SC, et al. Enhancing antimicrobial resistance strategies: Leveraging artificial intelligence for improved outcomes. S Afr J Chem Eng. 2025;51:272–286.
  9. Ruike ZH, Junqi ZH, Rongchen DA, Yating NI, Yingchun XU, Li ZH. Applications and clinical significance of artificial intelligence in antimicrobial resistance. Med J Peking Union Med Coll Hosp. 2025;16(5):1088–1095.
  10. Zubair A, Fazil M, Jawad M, Wdidi S. The role of machine learning in addressing antibiotic resistance: A new era in infectious disease control. MicrobiologyOpen. 2025;14(6):e70160.
  11. Taha BA, Ahmed NM, Talreja RK, Haider AJ, Al Mashhadany Y, Al-Jubouri Q, et al. Synergizing nanomaterials and artificial intelligence in advanced optical biosensors for precision antimicrobial resistance diagnosis. ACS Synth Biol. 2024;13(6):1600–1620.
  12. Lawal O, Elechi K, Adekunle F, Farinde O, Kolapo T, Igbokwe C, et al. A review on artificial intelligence and point-of-care diagnostics to combat antimicrobial resistance in resource-limited healthcare settings like Nigeria. J Pharma Insights Res. 2025;3(2):166–175.
  13. Bhattacharya R, Bose D, Siddique KR, Rodriguez RV, Ray A. Artificial intelligence for sustainable solutions in combating antimicrobial resistance through data driven health innovations. Discov Public Health. 2026;23(1):10.
  14. World Health Organization. Global antibiotic resistance surveillance report 2025: WHO global antimicrobial resistance and use surveillance system (GLASS). Geneva: World Health Organization; 2025.
  15. Feldgarden M, Brover V, Gonzalez-Escalona N, Frye JG, Haendiges J, Haft DH, et al. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep. 2021;11(1):12728.
  16. Munk P, Brinch C, Møller FD, Petersen TN, Hendriksen RS, Seyfarth AM, et al. Genomic analysis of sewage from 101 countries reveals global landscape of antimicrobial resistance. Nat Commun. 2022;13(1):7251.
  17. Singer AC, Thompson JR, Filho CR, Street R, Li X, Castiglioni S, et al. A world of wastewater-based epidemiology. Nat Water. 2023;1(5):408–415.
  18. Szymczak P, Szczurek E. Artificial intelligence-driven antimicrobial peptide discovery. Curr Opin Struct Biol. 2023;83:102733.
  19. Valavarasu S, Sangu Y, Mahapatra T. Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning. Sci Rep. 2025;15(1):30509.
  20. Howard A, Reza N, Green PL, Yin M, Duffy E, Mwandumba HC, et al. Artificial intelligence and infectious diseases: Tackling antimicrobial resistance, from personalised care to antibiotic discovery. Lancet Infect Dis. 2026;26(3):e181–e192.
  21. de la Fuente-Nunez C, Collins JJ. Essay: Using machine learning for antibiotic discovery. Phys Rev Lett. 2025;135(3):030001.
  22. Hossan MS, Gadelrab HA, Luo M, Guo D, Valenzuela Faccini N, Luo J. The role of immunomodulatory nanosystems in the treatment of sepsis: Past, present, and future. Nanomedicine. 2026;21(4):605–627.
  23. Leng T, Wan F, Torres MD, de la Fuente-Nunez C. Predicting and generating antibiotics against future pathogens with ApexOracle. arXiv [Preprint]. 2025;arXiv:2507.07862.
  24. Boudza R, Bounou S, Segura-Garcia J, Moukadiri I, Maicas S. Artificial intelligence as a catalyst for antimicrobial discovery: From predictive models to de novo design. Microorganisms. 2026;14(2):394.
  25. Agboeze T, Daramola O, Akomolafe A, Adedeji R, Markwei J. A predictive algorithm for the analysis of AMR trends and healthcare decision support. Wellcome Open Res. 2024;9:274.
  26. Nateghi Haredasht F, Amrollahi F, Maddali MV, Marshall N, Ma SP, Cooper LN, et al. Antibiotic Resistance Microbiology Dataset (ARMD): A resource for antimicrobial resistance from EHRs. Sci Data. 2025;12(1):1299.
  27. Seale AC, Gordon NC, Islam J, Peacock SJ, Scott JA. AMR surveillance in low and middle-income settings: A roadmap for participation in the Global Antimicrobial Surveillance System (GLASS). Wellcome Open Res. 2017;2:92.
  28. Faiyazuddin M, Rahman SJ, Anand G, Siddiqui RK, Mehta R, Khatib MN, et al. The impact of artificial intelligence on healthcare: A comprehensive review of advancements in diagnostics, treatment, and operational efficiency. Health Sci Rep. 2025;8(1):e70312.
  29. van Groesen E, Mons E, Kotsogianni I, Arts M, Tehrani KH, Wade N, et al. Semisynthetic guanidino lipoglycopeptides with potent in vitro and in vivo antibacterial activity. Sci Transl Med. 2024;16(759):eabo4736.
  30. Calero-Cáceres W, Tavella RA, Sellera FP, Balcazar JL, Rodriguez-Manzano J, Cayô R, et al. Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems. npj Antimicrob Resist. 2026;4(1):20.
Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 04/06/2026
Accepted 13/06/2026
Published 25/06/2026
Publication Time 21 Days
35

Login


My IP

PlumX Metrics