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image of Artificial Intelligence in the Management of One Health: An Update

Abstract

The One Health concept emphasizes the complex connection between environmental, animal, and human health and calls for cross-sectoral cooperation to improve ecological integrity and advance world health. The need for coordinated, preventative measures has grown more pressing as the frequency and complexity of new health risks caused by urbanization, globalization, and climate change increase. In this regard, current developments in machine learning (ML) and artificial intelligence (AI) are revolutionizing the One Health paradigm by greatly enhancing our ability to monitor, diagnose, and predict diseases. Predictive analytics, deep learning models, and decision support systems are examples of AI-driven technologies that help identify outbreaks early, allocate resources optimally, and reduce the cognitive load on medical staff. Predicting the spread of zoonotic illnesses, tracking antimicrobial resistance (AMR) trends, improving diagnostic precision, and guiding coordinated public health interventions are some of the main uses. Additionally, these technologies are being utilized to forecast health risks associated with pollution and habitat alteration, as well as to enhance environmental monitoring. In addition to highlighting the vital significance of international collaboration, moral leadership, and inclusive policymaking, this review broadens our knowledge of how AI and ML are transforming the One Health paradigm.

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2025-10-28
2025-12-24

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Artificial Intelligence in the Management of One Health: An Update
Bentham Science Publishers ; https://doi.org/10.2174/0115665240402515250929232435
/content/journals/cmm/10.2174/0115665240402515250929232435
/content/journals/cmm/10.2174/0115665240402515250929232435
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  • Article Type:     Review Article
Keywords: AI/ML ; deep learning ; treatment ; AMR ; zoonosis ; one health

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