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

Area of Science:

• Global Health
• Environmental Science
• Veterinary Medicine
• Computer Science

Background:

• The One Health concept highlights interconnected environmental, animal, and human health.
• Increasing global health risks necessitate coordinated, preventative strategies.
• Urbanization, globalization, and climate change exacerbate health challenges.

Purpose of the Study:

• To review the transformative impact of AI and ML on the One Health paradigm.
• To explore AI/ML applications in disease monitoring, diagnosis, and prediction.
• To underscore the role of AI/ML in addressing environmental and public health risks.

Main Methods:

• Review of current AI and ML technologies applied to One Health.
• Analysis of AI-driven tools such as predictive analytics and deep learning models.
• Examination of AI applications in disease surveillance and environmental monitoring.

Main Results:

• AI and ML significantly enhance disease outbreak identification and prediction.
• These technologies optimize resource allocation and support public health interventions.
• AI/ML aids in forecasting health risks from pollution and habitat changes.
• Improved environmental monitoring capabilities through AI/ML.

Conclusions:

• AI and ML are pivotal in advancing the One Health agenda.
• International collaboration and inclusive policymaking are crucial for leveraging AI/ML.
• AI/ML offers powerful tools for managing complex, interconnected health threats.