@article{fdi:010081100,
  title = {{R}esearch perspectives on animal health in the era of artificial intelligence},
  author = {{E}zanno, {P}. and {P}icault, {S}. and {B}eaunee, {G}. and {B}ailly, {X}. and {M}unoz, {F}. and {D}uboz, {R}. and {M}onod, {H}. and {G}u{\'e}gan, {J}ean-{F}ran{\c{c}}ois},
  editor = {},
  language = {{ENG}},
  abstract = {{L}everaging 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 hostxpathogen 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. {I}n turn, challenges in {AH} may stimulate {AI} research due to specificity of {AH} systems, data, constraints, and analytical objectives. {B}ased on a literature review of scientific papers at the interface between {AI} and {AH} covering the period 2009-2019, and interviews with {F}rench 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. {A}fter presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the {AH}/{AI} interface. {W}ith 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.},
  keywords = {{A}nimal disease ; {D}ata ; {L}ivestock ; {M}odelling ; {A}rtificial intelligence ; {D}ecision support tool},
  booktitle = {},
  journal = {{V}eterinary {R}esearch},
  volume = {52},
  numero = {1},
  pages = {40 [15 p.]},
  ISSN = {0928-4249},
  year = {2021},
  DOI = {10.1186/s13567-021-00902-4},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081100},
}