Research perspectives on animal health in the era of artificial intelligence

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to...

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Vydáno v:Veterinary research (Paris) Ročník 52; číslo 1; s. 40
Hlavní autoři: Ezanno, Pauline, Picault, Sébastien, Beaunée, Gaël, Bailly, Xavier, Muñoz, Facundo, Duboz, Raphaël, Monod, Hervé, Guégan, Jean-François
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 06.03.2021
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Animal biology
Animal disease
animal health
Animals
Artificial Intelligence
Artificial Intelligence - statistics & numerical data
Climate change
Computer Science
Data
Decision making
Decision support tool
Deep learning
Delivery of Health Care - methods
Disease
Epidemics
Epidemiology
humans
Immunology
Interfaces
Life Sciences
Livestock
Medicine
Medicine & Public Health
Microbiology
Modelling
Pathogens
Review
risk
Risk assessment
veterinary medicine
Veterinary Medicine - instrumentation
Veterinary Medicine - methods
Veterinary medicine and animal Health
Veterinary Medicine/Veterinary Science
Virology
Animal disease
Data
Livestock
Modelling
Artificial intelligence
Decision support tool
ISSN:1297-9716, 0928-4249, 1297-9716
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Shrnutí:Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.
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ISSN:1297-9716
0928-4249
1297-9716
DOI:10.1186/s13567-021-00902-4