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Related Experiment Video

Updated: Jun 26, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Using Evidence-Based Veterinary Medicine and Artificial Intelligence to Support Clinical Decision Making in

Sally Everitt¹, Caroline Scobie²

¹Evidence, RCVS Knowledge, 1 Hardwick Street, London, EC1R 4RB, UK.

The Veterinary Clinics of North America. Small Animal Practice

|June 24, 2026

Summary

Veterinary clinical decision-making can be improved using evidence-based medicine and artificial intelligence tools to overcome cognitive biases and memory limitations inherent in human thinking.

Keywords:

Artificial intelligence Bias Clinical decision-making Diagnosis Evidence-based veterinary medicine

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Last Updated: Jun 26, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Veterinary Medicine
Cognitive Science
Artificial Intelligence

Background:

Clinical decision-making is crucial in veterinary practice for diagnosis and treatment.
Human decision-making pathways are susceptible to cognitive biases and working memory limitations.

Purpose of the Study:

To explore how evidence-based veterinary medicine principles can support clinical decision-making.
To investigate the role of artificial intelligence tools in enhancing veterinary clinical decisions.

Main Methods:

Review of principles of evidence-based veterinary medicine.
Analysis of artificial intelligence tools applicable to clinical decision support.
Integration of cognitive science insights into veterinary decision-making frameworks.

Main Results:

Evidence-based veterinary medicine provides a structured approach to mitigate bias.
Artificial intelligence offers tools to augment diagnostic and treatment planning capabilities.
Combining these approaches can enhance accuracy and efficiency in veterinary practice.

Conclusions:

Evidence-based veterinary medicine and artificial intelligence are valuable assets for improving veterinary clinical decision-making.
These strategies can help overcome inherent human cognitive limitations in practice.
Further research into AI integration can optimize veterinary patient care.