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Related Experiment Video

Updated: Dec 6, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Artificial Intelligence and One Health: Knowledge Bases for Causal Modeling.

Nitin Pandit1, Abi T Vanak1,2,3

  • 1Ashoka Trust for Research in Ecology and the Environment (ATREE), Bangalore, 560064 India.

Journal of the Indian Institute of Science
|October 13, 2020
PubMed
Summary

Building comprehensive One Health databases is crucial for managing zoonotic diseases. Advanced knowledge bases using artificial intelligence are needed to model complex human-ecosystem health relationships effectively.

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Area of Science:

  • One Health
  • Zoonotic Disease Epidemiology
  • Computational Biology

Background:

  • Global efforts focus on One Health databases to combat zoonotic disease outbreaks.
  • Simple databases are insufficient for modeling complex human-ecosystem health interdependencies due to incomplete information.

Purpose of the Study:

  • To highlight the necessity of advanced knowledge bases beyond traditional databases.
  • To propose the integration of artificial intelligence for improved causal modeling in One Health.

Main Methods:

  • Utilizing non-monotonic logic-based artificial intelligence techniques.
  • Complementing traditional databases with knowledge bases.

Main Results:

  • Causal models can be iteratively improved with new and potentially contradictory field data.
  • Advanced AI-driven knowledge bases enhance the understanding of zoonotic disease dynamics.

Conclusions:

  • Effective management of zoonotic diseases requires sophisticated, AI-enhanced One Health knowledge systems.
  • Integrating AI with field observations is key to building robust causal models for ecosystem and human health.