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 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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Subjects: | L73 - Maladies des animaux, maladie des animaux, santé animale, intelligence artificielle, modélisation, système d'aide à la décision, épidémiologie, http://aims.fao.org/aos/agrovoc/c_426, http://aims.fao.org/aos/agrovoc/c_431, http://aims.fao.org/aos/agrovoc/c_27064, http://aims.fao.org/aos/agrovoc/c_230ab86c, http://aims.fao.org/aos/agrovoc/c_49868, http://aims.fao.org/aos/agrovoc/c_2615, |
Online Access: | http://agritrop.cirad.fr/597870/ http://agritrop.cirad.fr/597870/1/Ezanno%20et%20al.%20-%202021%20-%20Research%20perspectives%20on%20animal%20health%20in%20the%20era%20.pdf |
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dig-cirad-fr-5978702024-01-29T03:25:13Z http://agritrop.cirad.fr/597870/ http://agritrop.cirad.fr/597870/ Research perspectives on animal health in the era of artificial intelligence. Ezanno Pauline, Picault Sébastien, Beaunée Gaël, Bailly Xavier, Munoz Facundo, Duboz Raphaël, Monod Hervé, Guégan Jean-François. 2021. Veterinary Research, 52:40, 15 p.https://doi.org/10.1186/s13567-021-00902-4 <https://doi.org/10.1186/s13567-021-00902-4> Research perspectives on animal health in the era of artificial intelligence Ezanno, Pauline Picault, Sébastien Beaunée, Gaël Bailly, Xavier Munoz, Facundo Duboz, Raphaël Monod, Hervé Guégan, Jean-François eng 2021 Veterinary Research L73 - Maladies des animaux maladie des animaux santé animale intelligence artificielle modélisation système d'aide à la décision épidémiologie http://aims.fao.org/aos/agrovoc/c_426 http://aims.fao.org/aos/agrovoc/c_431 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_2615 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. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/597870/1/Ezanno%20et%20al.%20-%202021%20-%20Research%20perspectives%20on%20animal%20health%20in%20the%20era%20.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1186/s13567-021-00902-4 10.1186/s13567-021-00902-4 info:eu-repo/semantics/altIdentifier/doi/10.1186/s13567-021-00902-4 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1186/s13567-021-00902-4 info:eu-repo/grantAgreement/EC/H2020/874850//(EU) MOnitoring Outbreak events for Disease surveillance in a data science context/MOOD |
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L73 - Maladies des animaux maladie des animaux santé animale intelligence artificielle modélisation système d'aide à la décision épidémiologie http://aims.fao.org/aos/agrovoc/c_426 http://aims.fao.org/aos/agrovoc/c_431 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_2615 L73 - Maladies des animaux maladie des animaux santé animale intelligence artificielle modélisation système d'aide à la décision épidémiologie http://aims.fao.org/aos/agrovoc/c_426 http://aims.fao.org/aos/agrovoc/c_431 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_2615 |
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L73 - Maladies des animaux maladie des animaux santé animale intelligence artificielle modélisation système d'aide à la décision épidémiologie http://aims.fao.org/aos/agrovoc/c_426 http://aims.fao.org/aos/agrovoc/c_431 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_2615 L73 - Maladies des animaux maladie des animaux santé animale intelligence artificielle modélisation système d'aide à la décision épidémiologie http://aims.fao.org/aos/agrovoc/c_426 http://aims.fao.org/aos/agrovoc/c_431 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_2615 Ezanno, Pauline Picault, Sébastien Beaunée, Gaël Bailly, Xavier Munoz, Facundo Duboz, Raphaël Monod, Hervé Guégan, Jean-François Research perspectives on animal health in the era of artificial intelligence |
description |
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. |
format |
article |
topic_facet |
L73 - Maladies des animaux maladie des animaux santé animale intelligence artificielle modélisation système d'aide à la décision épidémiologie http://aims.fao.org/aos/agrovoc/c_426 http://aims.fao.org/aos/agrovoc/c_431 http://aims.fao.org/aos/agrovoc/c_27064 http://aims.fao.org/aos/agrovoc/c_230ab86c http://aims.fao.org/aos/agrovoc/c_49868 http://aims.fao.org/aos/agrovoc/c_2615 |
author |
Ezanno, Pauline Picault, Sébastien Beaunée, Gaël Bailly, Xavier Munoz, Facundo Duboz, Raphaël Monod, Hervé Guégan, Jean-François |
author_facet |
Ezanno, Pauline Picault, Sébastien Beaunée, Gaël Bailly, Xavier Munoz, Facundo Duboz, Raphaël Monod, Hervé Guégan, Jean-François |
author_sort |
Ezanno, Pauline |
title |
Research perspectives on animal health in the era of artificial intelligence |
title_short |
Research perspectives on animal health in the era of artificial intelligence |
title_full |
Research perspectives on animal health in the era of artificial intelligence |
title_fullStr |
Research perspectives on animal health in the era of artificial intelligence |
title_full_unstemmed |
Research perspectives on animal health in the era of artificial intelligence |
title_sort |
research perspectives on animal health in the era of artificial intelligence |
url |
http://agritrop.cirad.fr/597870/ http://agritrop.cirad.fr/597870/1/Ezanno%20et%20al.%20-%202021%20-%20Research%20perspectives%20on%20animal%20health%20in%20the%20era%20.pdf |
work_keys_str_mv |
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