Evidence map›Paper›PMID 40100535›Full record

ReviewMicrobial biotechnology2025

Microbial Technologies Enhanced by Artificial Intelligence for Healthcare Applications.

Taeho Yu, Minjee Chae, Ziling Wang, Gahyeon Ryu, Gi Bae Kim, Sang Yup Lee

Abstract readReview
In one paragraph

Review in Microbial biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Taeho YuMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Minjee ChaeMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Ziling WangMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Gahyeon RyuMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Gi Bae KimMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Sang Yup LeeMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.ORCID https://orcid.org/0000-0003-0599-3091

Funding

KAIST Cross-Generation Collaborative Lab projectNational Research Foundation of Korea 2021M3A9I5023245
6 · The paper itself

Abstract

The combination of artificial intelligence (AI) with microbial technology marks the start of a major transformation, improving applications throughout biotechnology, especially in healthcare. With the capability of AI to process vast amounts of biological big data, advanced microbial technology allows for a comprehensive understanding of complex biological systems, advancing disease diagnosis, treatment and the development of microbial therapeutics. This mini review explores the impact of AI-integrated microbial technologies in healthcare, highlighting advancements in microbial biomarker-based diagnosis, the development of microbial therapeutics and the microbial production of therapeutic compounds. This exploration promises significant improvements in the design and implementation of health-related solutions, steering a new era in biotechnological applications.

Indexed as

Artificial IntelligenceBiotechnologyMicrobiological TechniquesDelivery of Health CareHumansartificial intelligencedrughealthcaremicrobial technology

Identifiers

PMID40100535
PMCPMC11917392

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.