Cited by 37Probiotics and Fermented FoodsGut microbiota and healthAnimal Nutrition and Physiology

Understanding the Functionality of Probiotics on the Edge of Artificial Intelligence (AI) Era

Open access
Asar, Remziye, Erenler, Sinem, Devecioglu, Dilara, Ispirli, Humeyra, Karbancioglu-Guler, Funda, Ozturk, Hale Inci, Dertli, Enes·2025·Fermentation

Abstract

This review focuses on the potential utilization of artificial intelligence (AI) tools to deepen our understanding of probiotics, their mode of action, and technological characteristics such as survival. To that end, this review provides an overview of the current knowledge on probiotics as well as next-generation probiotics. AI-aided omics technologies, including genomics, transcriptomics, and proteomics, offer new insights into the genetic and functional properties of probiotics. Furthermore, AI can be used to elucidate key probiotic activities such as microbiota modulation, metabolite production, and immune system interactions to enable an improved understanding of their health impacts. Additionally, AI technologies facilitate precision in identifying probiotic health impacts, including their role in gut health, anticancer activity, and antiaging effects. Beyond health applications, AI can expand the technological use of probiotics, optimizing storage survival and broadening biotechnological approaches. In this context, this review addresses how AI-driven approaches can be facilitated by strengthening the evaluation of probiotic characteristics, explaining their mechanisms of action, and enhancing their technological applications. Moreover, the potential of AI to enhance the precision of probiotic health impact assessments and optimize industrial applications is highlighted, concluding with future perspectives on the transformative role of AI in probiotic research.

Integrity signals

Registry

Crossref · 10.3390/fermentation11050259

Cites

199 of 199 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI capabilities are fueling probiogenomics, where computational models predict how a probiotic's genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects.

    “The growth of AI capabilities is fueling new fields, such as probiogenomics, where computational models are used to predict how a probiotic’s genome will interact with the host immune system or existing microbiota, ensuring stable colonization without side effects [164,169].”

    From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [164]

  • AI can analyze big data from omics technologies to create a safety profile for probiotics based on metabolomics, evaluating metabolite production to ensure strains do not produce harmful substances associated with health risks.

    “AI can be used to analyze big data from various omics technologies to create a safety profile for probiotics based on, for example, metabolomics, where AI evaluates metabolite production to ensure that strains do not produce harmful substances associated with various health risks [156,164].”

    From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [164]

  • Computational tools are evolving and becoming integrated into regulatory processes, where genomic and bioinformatic analysis complement phenotypic testing, highlighting the need for standardized, transparent, and validated computational processes.

    “These computational tools are evolving and becoming integrated into regulatory processes over time, where genomic and bioinformatic analysis complement phenotypic testing to facilitate safety assessment and documentation, and highlight the need for standardized, transparent, and validated computational processes [163,164].”

    From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [164]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Asar, R., Erenler, S., Devecioglu, D., Ispirli, H., Karbancioglu-Guler, F., Ozturk, H. I., & Dertli, E.
Affiliations
  • Yıldız Technical University (TR)
  • Istanbul Technical University (TR)
  • Food Engineering Department, Chemical and Metallurgical Engineering Faculty, İstanbul Technical University, Istanbul 34437, Türkiye
  • Food Engineering Department, Engineering Faculty, Bayburt University, Bayburt 69100, Türkiye
  • Food Engineering Department, Chemical and Metallurgical Engineering Faculty, Yildiz Technical University, Istanbul 34349, Türkiye
Corresponding author
Dertli, Enes
Venue
Fermentation
Publisher
MDPI AG
Published
2025-05-05
Volume
11
Issue
5
Pages
259
ISSN
2311-5637 (ISSN-L)
OpenAlex
W4410081544
Cited by
37
References
199
Citations by year
2026: 24 · 2025: 13
FWCI
17.33
Type
journal article
Language
en
Open access
Locations
  • Fermentation (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DSpace@Bayburt (submittedVersion, other-oa)
  • Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Agricultural and Biological Sciences › Animal Science and Zoology › Animal Nutrition and Physiology
Keywords
probiotics, artificial intelligence, probiotic viability, microbiota modulation, metabolite production, immune interactions, gut health, anticancer activity, anti-aging effects, next-generation probiotics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review