Artificial Intelligence in Microbiological Research: Methods, Applications and Implications

Year : 2026 | Volume : 16 | Issue : 02 | Page : 22 36
By

V. Basil Hans,

  1. Research Professor, Department of Management and Commerce Srinivas University Mangalore, Karnataka, India

Abstract

Artificial Intelligence (AI) is revolutionising microbiological research by enabling the rapid analysis of complex biological data and improving the accuracy, efficiency, and reliability of scientific investigations. Recent advances in machine learning, deep learning, and bioinformatics have transformed AI into a powerful tool for studying microorganisms, their genetic composition, evolutionary patterns, and interactions with hosts and the environment. AI-driven computational models can process large and complex datasets far more efficiently than conventional analytical methods, allowing researchers to identify meaningful patterns and generate accurate predictions in a shorter period. AI has numerous applications in microbiology, including microbial identification and classification, prediction of antimicrobial resistance, microbial genome analysis, disease diagnosis, outbreak surveillance, and drug discovery. Advanced image recognition algorithms assist in the rapid identification of bacterial, viral, and fungal pathogens, while genomic analysis tools facilitate the detection of mutations, virulence factors, and resistance genes. AI also supports the development of precision medicine by enabling personalised treatment strategies based on microbial and patient-specific data. Furthermore, AI-powered epidemiological models help monitor the spread of infectious diseases, predict potential outbreaks, and assist public health authorities in implementing timely preventive measures. Despite its significant advantages, the integration of AI into microbiological research faces several challenges. These include concerns regarding data quality and availability, model interpretability, algorithmic bias, privacy and security of biological data, and ethical and regulatory considerations. Addressing these issues is essential for ensuring the responsible and reliable use of AI technologies in healthcare and research. As computational technologies continue to advance, AI is expected to play an increasingly important role in microbiology by accelerating scientific discoveries, improving diagnostic accuracy, supporting the development of novel antimicrobial agents and vaccines, and strengthening global public health initiatives. This article discusses the diverse applications, advantages, challenges, and prospects of artificial intelligence in microbiological research.

Keywords: Analysis of the microbiome predictive modelling, infectious disease, big data analytics, computational biology, personalised medicine

[This article belongs to Research and Reviews: A Journal of Microbiology and Virology ]

How to cite this article: V. Basil Hans. Artificial Intelligence in Microbiological Research: Methods, Applications and Implications. Research and Reviews: A Journal of Microbiology and Virology. 2026; 16(02):22-36.
How to cite this URL: V. Basil Hans. Artificial Intelligence in Microbiological Research: Methods, Applications and Implications. Research and Reviews: A Journal of Microbiology and Virology. 2026; 16(02):22-36. Available from: https://journals.stmjournals.com/rrjomv/article=2026/view=249058

References

  1. Shelke YP, Badge AK, Bankar NJ, Badge A. Applications of artificial intelligence in microbial diagnosis. Cureus. 2023 Nov 24;15(11).
  2. Yakimovich A. Toward the novel AI tasks in infection biology. mSphere. 2024 Feb 28;9(2):e00591–23.
  3. Santoyo AT, Lopera C, Vásquez AL, Fernández FS, Pérez IG, Chumbita M, et al. Identifying the most important data for research in the field of infectious diseases: Thinking on the basis of artificial intelligence. Rev Esp Quimioter. 2023 Aug 12;36(6):592.
  4. Dudek NK, Chakhvadze M, Kobakhidze S, Kantidze O, Gankin Y. Supervised machine learning for microbiomics: Bridging the gap between current and best practices. Mach Learn Appl. 2024 Dec 1;18:100607.
  5. Rani P, Kotwal S, Manhas J, Sharma V, Sharma S. Machine learning and deep learning based computational approaches in automatic microorganisms image recognition: Methodologies, challenges, and developments. Arch Comput Methods Eng. 2022 May;29(3):1801–37.
  6. Zhang J, Li C, Yin Y, Zhang J, Grzegorzek M. Applications of artificial neural networks in microorganism image analysis: A comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artif Intell Rev. 2023 Feb;56(2):1013–70.
  7. Van den Bogert B, Boekhorst J, Pirovano W, May A. On the role of bioinformatics and data science in industrial microbiome applications. Front Genet. 2019 Aug 9;10:721.
  8. Roy G, Prifti E, Belda E, Zucker JD. Deep learning methods in metagenomics: A review. Microb Genom. 2024 Apr 17;10(4):001231.
  9. Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, et al. Antimicrobial resistance crisis: Could artificial intelligence be the solution? Mil Med Res. 2024 Jan 23;11(1):7.
  10. Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, et al. Applications of machine learning in human microbiome studies: A review on feature selection, biomarker identification, disease prediction and treatment. Front Microbiol. 2021 Feb 19;12:634511.
  11. Narayana J, Mac Aogáin M, Goh WWB, Xia K, Tsaneva-Atanasova K, Chotirmall SH. Mathematical-based microbiome analytics for clinical translation. 2021:6272–6281.
  12. Rusic D, Kumric M, Seselja Perisin A, Leskur D, Bukic J, Modun D, et al. Tackling the antimicrobial resistance “pandemic” with machine learning tools: A summary of available evidence. Microorganisms. 2024 Apr 23;12(5):842.
  13. Rau MH, Zeidan AA. Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans. 2018 Apr 17;46(2):249–60.
  14. Chen H, Martin B, Daimon CM, Maudsley S. Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications. Front Physiol. 2013 Jan 30;4:8.
  15. Pizzuto G, Wang H, Fakhruldeen H, Peng B, Luck KS, Cooper AI. Accelerating laboratory automation through robot skill learning for sample scraping. In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE); 2024 Aug 28. p. 2103–2110. IEEE.
  16. Börnigen D, Moon YS, Rahnavard G, Waldron L, McIver L, Shafquat A, et al. A reproducible approach to high-throughput biological data acquisition and integration. PeerJ. 2015 Mar 31;3:e791.
  17. Pacheco AR, Pauvert C, Kishore D, Segrè D. Toward FAIR representations of microbial interactions. mSystems. 2022 Oct 26;7(5):e00659–22.
  18. Toh TS, Dondelinger F, Wang D. Looking beyond the hype: Applied AI and machine learning in translational medicine. EBioMedicine. 2019 Sep 1;47:607–15.
  19. Templin T, Perez MW, Sylvia S, Leek J, Sinnott-Armstrong N. Addressing 6 challenges in generative AI for digital health: A scoping review. PLoS Digit Health. 2024 May 23;3(5):e0000503.
  20. Budach L, Feuerpfeil M, Ihde N, Nathansen A, Noack N, Patzlaff H, et al. The effects of data quality on machine learning performance. arXiv preprint arXiv:2207.14529. 2022 Jul 29.
  21. Bhaskar SM. The impact of the COVID-19 pandemic on e-services and digital tools development in medicine. In: Cardiovascular complications of COVID-19: Acute and long-term impacts. Cham: Springer International Publishing; 2023 Jan 1. p. 413–427.
  22. Undheim TA. The whack-a-mole governance challenge for AI-enabled synthetic biology: Literature review and emerging frameworks. Front Bioeng Biotechnol. 2024 Feb 28;12:1359768.
  23. Sandbrink JB. Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools [Internet]. arXiv.org. 2023. Available from: https://arxiv.org/abs/2306.13952.
  24. Gervais D. Exploring the interfaces between big data and intellectual property law. J Intell Prop Info Tech & Elec Com L. 2019;10:3.
  25. Al-Amran FG, Hezam AM, Salman R, Yousif MG. Genomic Analysis and Artificial Intelligence: Predicting Viral Mutations and Future Pandemics [Internet]. arXiv.org. 2023. Available from: https://arxiv.org/abs/2309.15936.
  26. Rodríguez-González A, Zanin M, Menasalvas-Ruiz E. Public health and epidemiology informatics: Can artificial intelligence help future global challenges? An overview of antimicrobial resistance and impact of climate change in disease epidemiology. Yearb Med Inform. 2019 Aug;28(01):224–31.
  27. Gray K, Slavotinek J, Dimaguila GL, Choo D. Artificial intelligence education for the health workforce: Expert survey of approaches and needs. JMIR Med Educ. 2022 Apr 4;8(2):e35223.
  28. Teng M, Singla R, Yau O, Lamoureux D, Gupta A, Hu Z, et al. Health care students’ perspectives on artificial intelligence: Countrywide survey in Canada. JMIR Med Educ. 2022 Jan 31;8(1):e33390.
  29. Weidener L, Fischer M. Artificial intelligence teaching as part of medical education: Qualitative analysis of expert interviews. JMIR Med Educ. 2023 Apr 24;9(1):e46428.
  30. Tanaka M, Matsumura S, Bito S. Roles and competencies of doctors in artificial intelligence implementation: Qualitative analysis through physician interviews. JMIR Form Res. 2023 May 18;7(1):e46020.
Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 09/06/2026
Accepted 28/06/2026
Published 29/06/2026
Publication Time 20 Days

Login

My IP

PlumX Metrics

Support