Artificial Intelligence and Machine Learning in Clinical Microbiology: Advancing Diagnostics with Responsible Innovation

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Keywords:

Artificial intelligence, Clinical laboratory, Diagnostic techniques, Machine learning, Microbiology

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References

[1]. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.

[2]. Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, et al. Machine learning for clini-cal decision support in infectious diseases: a narrative re-view. Clin Microbiol Infect. 2020;26(5):584–95.

[3]. van Belkum A, Bachmann TT, Lüdke G, Lisby JG, Kahlmeter G, Mohess A, et al. Developmental roadmap for antimicrobial susceptibility testing systems. Nat Rev Microbiol. 2019;17(1):51–62.

[4]. Bassetti M, Peghin M, Vena A, Giacobbe DR. Artificial intelligence in infectious diseases. Clin Microbiol Infect. 2024;30(3):301–8.

[5]. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and chal-lenges. Brief Bioinform. 2018;19(6):1236–46.

[6]. World Health Organization. Global antimicrobial re-sistance and use surveillance system (GLASS) report 2024. Geneva: World Health Organization; 2024. Avail-able from https://www.who.int/initiatives/glass

[7]. Nature Medicine. Artificial intelligence in medicine must be transparent and trustworthy. Nat Med. 2024;30:1–2.

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Published

13-10-2026

How to Cite

1.
Muddassir M. Artificial Intelligence and Machine Learning in Clinical Microbiology: Advancing Diagnostics with Responsible Innovation. Chron Biomed Sci [Internet]. 2026 Oct. 13 [cited 2026 Oct. 14];3(1):PID74. Available from: https://cbsciences.us/index.php/cbs/article/view/74