Evidence map›Paper›PMID 37140603›Full record

ReviewBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2023

[Application areas of artificial intelligence in the context of One Health with a focus on antimicrobial resistance].

Christopher Irrgang, Tim Eckmanns, Max V Kleist, Esther-Maria Antão, Katharina Ladewig, Lothar H Wieler, Nils Körber

Abstract readEnglish AbstractReview
In one paragraph

Review in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Christopher IrrgangZentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland. IrrgangC@rki.de.
Tim EckmannsFG 37: Nosokomiale Infektionen, Surveillance von Antibiotikaresistenz und -verbrauch, Robert Koch-Institut, Berlin, Deutschland.
Max V KleistFachbereich für Mathematik und Informatik, Freie Universität Berlin, Berlin, Deutschland.
Esther-Maria AntãoFachgebiet Digital Global Public Health, Hasso-Plattner-Institut, Potsdam, Deutschland.
Katharina LadewigZentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland.
Lothar H WielerRobert Koch-Institut, Berlin, Deutschland.
Nils KörberZentrum für Künstliche Intelligenz in der Public Health-Forschung, Robert Koch-Institut, Wildau, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Societal health is facing a number of new challenges, largely driven by ongoing climate change, demographic ageing, and globalization. The One Health approach links human, animal, and environmental sectors with the goal of achieving a holistic understanding of health in general. To implement this approach, diverse and heterogeneous data streams and types must be combined and analyzed. To this end, artificial intelligence (AI) techniques offer new opportunities for cross-sectoral assessment of current and future health threats. Using the example of antimicrobial resistance as a global threat in the One Health context, we demonstrate potential applications and challenges of AI techniques.This article provides an overview of different applications of AI techniques in the context of One Health and highlights their challenges. Using the spread of antimicrobial resistance (AMR), an increasing global threat, as an example, existing and future AI-based approaches to AMR containment and prevention are described. These range from novel drug development and personalized therapy, to targeted monitoring of antibiotic use in livestock and agriculture, to comprehensive environmental surveillance.

Indexed as

Anti-Bacterial AgentsOne HealthAnimalsArtificial IntelligenceDrug Resistance, BacterialGermanyHumansAnti-Bacterial AgentsAntimicrobial resistanceArtificial intelligenceHealthOne HealthSociety

Identifiers

PMID37140603
PMCPMC10157576

What Socratic 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 Socratic graph.