AI in microbiome‐related healthcare
Open accessAbstract
Artificial intelligence (AI) has the potential to transform clinical practice and healthcare. Following impressive advancements in fields such as computer vision and medical imaging, AI is poised to drive changes in microbiome-based healthcare while facing challenges specific to the field. This review describes the state-of-the-art use of AI in microbiome-related healthcare. It points out limitations across topics such as data handling, AI modelling and safeguarding patient privacy. Furthermore, we indicate how these current shortcomings could be overcome in the future and discuss the influence and opportunities of increasingly complex data on microbiome-based healthcare.
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AI-based forecasts still require careful experimental and clinical verification to ensure human safety due to the lack of a unified validation system for methods.
“Despite their potential, AI-based forecasts still require careful experimental and clinical verification to ensure human safety due to the lack of a unified validation system for methods [173].”
From Current Concepts in Probiotic Safety and Efficacy (2026) · cites this paper as [173]
The ‘Current limitations and future perspectives’ section says that few AI models have reached clinical application, often because they lack independent validation, and calls for independent test data. It does not say that there is no unified validation system or specifically require experimental and clinical verification to ensure human safety. Page numbers were not available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI solutions demand standardized datasets, transparent reporting, and interpretable models to mitigate overfitting risks and address ethical concerns.
“Despite these transformative capabilities, AI solutions demand standardized datasets (including standardized sample collection and processing), transparent reporting, and interpretable models to mitigate overfitting risks and address ethical concerns (Probul et al., 2024).”
From Microbiota-centered interventions to boost immune checkpoint blockade therapies (2025) · cites this paper as Probul et al., 2024
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Federated machine learning in data-protection-compliant research
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Flagged first, then by citations
Research works printed but not linked: 2
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Cited by
Sorted by citations
Current Concepts in Probiotic Safety and Efficacy
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“Despite their potential, AI-based forecasts still require careful experimental and clinical verification to ensure human safety due to the lack of a unified validation system for methods [173].”
Microbiota-centered interventions to boost immune checkpoint blockade therapies
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- Authors
- Probul, N., Huang, Z., Saak, C. C., Baumbach, J., & List, M.
- Affiliations
- Universität Hamburg (DE)
- Technical University of Munich (DE)
- Corresponding author
- Baumbach, Jan
- Venue
- Microbial Biotechnology
- Publisher
- Wiley
- Published
- 2024-11
- Volume
- 17
- Issue
- 11
- Pages
- e70027-e70027
- ISSN
- 1751-7915 (ISSN-L)
- PMC
- PMC11530995
- OpenAlex
- W4404004411
- Cited by
- 36
- References
- 109
- Citations by year
- 2026: 20 · 2025: 16
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Microbial Biotechnology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01IS21079)
- Deutsche Forschungsgemeinschaft (395357507)
- China Scholarship Council
- HORIZON EUROPE Framework Programme (101079777)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, clinical microbiology, diagnostic imaging, computer vision, patient privacy
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Humans, Microbiota
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 ›
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