AI in microbiome-related healthcare

Niklas Probul; Zihua Huang; Christina Caroline Saak; Jan Baumbach; Markus List

doi:10.1111/1751-7915.70027

AI in microbiome-related healthcare

Niklas Probul
, Zihua Huang
, Christina Caroline Saak
, Jan Baumbach
, Markus List

Assistant Professorship of Data Science in Systems Biology

Universität Hamburg
Technical University of Munich
University of Southern Denmark

Research output: Contribution to journal › Short survey › peer-review

25 Scopus citations

Abstract

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.

Original language	English
Article number	e70027
Journal	Microbial Biotechnology
Volume	17
Issue number	11
DOIs	https://doi.org/10.1111/1751-7915.70027
State	Published - Nov 2024

Access to Document

10.1111/1751-7915.70027

Cite this

@article{15b8bb7fd4bd41689476e7e2fd5c9b54,

title = "AI in microbiome-related healthcare",

abstract = "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.",

author = "Niklas Probul and Zihua Huang and Saak, \{Christina Caroline\} and Jan Baumbach and Markus List",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Microbial Biotechnology published by John Wiley \& Sons Ltd.",

year = "2024",

month = nov,

doi = "10.1111/1751-7915.70027",

language = "English",

volume = "17",

journal = "Microbial Biotechnology",

issn = "1751-7907",

publisher = "John Wiley and Sons Ltd",

number = "11",

}

TY - JOUR

T1 - AI in microbiome-related healthcare

AU - Probul, Niklas

AU - Huang, Zihua

AU - Saak, Christina Caroline

AU - Baumbach, Jan

AU - List, Markus

PY - 2024/11

Y1 - 2024/11

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85208291996

U2 - 10.1111/1751-7915.70027

DO - 10.1111/1751-7915.70027

M3 - Short survey

C2 - 39487766

AN - SCOPUS:85208291996

SN - 1751-7907

VL - 17

JO - Microbial Biotechnology

JF - Microbial Biotechnology

IS - 11

M1 - e70027

ER -

AI in microbiome-related healthcare

Abstract

Access to Document

Other files and links

Fingerprint

Cite this