Abstract
Mobilizing Artificial Intelligence (AI) approaches in Animal Health (AH) makes it possible to address issues of high logical or algorithmic complexity such as those encountered in quantitative and predictive epidemiology, precision-based medicine, or to study host × pathogen relationships. AI can to some extent facilitate diagnosis and case detection, make predictions more reliable and reduce errors, allow more realistic representations of complex biological systems also readable by non-computer scientists, speed-up decisions, improve accuracy in risk analyses, and allow interventions to be better targeted and their effects anticipated. In addition, challenges in AH may stimulate AI research in turn due to the specificity of systems, data, constraints, and analytical objectives. Based on a literature review at the interface between AI and AH covering the period 2009- 2019, and interviews with French researchers positioned at this interface, this synthesis explains the main areas in AH in which AI is mobilized, how it contributes to revisiting AH research issues and removes methodological barriers, and how AH research questions stimulate new AI research development. After presenting the possible obstacles and levers, we propose recommendations to better grasp the challenge represented by this new AH/AI interface.