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Generative artificial intelligence provides accurate case selection in veterinary retrospective studies.

Armen M Brus1, Thomas Edwards1,2, Genna Atiee1

  • 1College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX.

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|December 10, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) tools, especially Gemini 2.5 Pro, show high accuracy in identifying cases for veterinary studies. This accelerates research by matching expert selections, but AI results require investigator verification.

Keywords:
agreementartificial intelligencecase selectiondata analysisretrospective

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Area of Science:

  • Veterinary Medicine
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Retrospective veterinary studies require accurate case identification based on inclusion/exclusion criteria.
  • Manual review of medical records is time-consuming and can be a bottleneck in research.

Purpose of the Study:

  • To evaluate the agreement between automation tools and expert evaluators in identifying cases for retrospective veterinary studies.
  • To compare the performance of different AI applications and a keyword search algorithm against expert judgment.

Main Methods:

  • Three study populations (trauma dogs, stent patients, cholecystectomy dogs) were assessed.
  • Expert reviewers compared medical records with AI tools (Gemini 2.5 Pro, NotebookLM) and a Python-based keyword search algorithm.
  • Processing time and agreement with expert selections were recorded.

Main Results:

  • Gemini 2.5 Pro demonstrated high accuracy (99%-100%) and fast processing times, closely matching expert selections across studies.
  • NotebookLM showed comparable performance for the stent dataset but was less accurate for others.
  • Python tools exhibited variable performance depending on the study.

Conclusions:

  • AI tools, particularly generative AI like Gemini 2.5 Pro, can effectively identify cases meeting study criteria, accelerating retrospective study development.
  • AI enhances speed and scalability in veterinary research, with potential applications in clinical practice.
  • Verification of AI-identified cases by investigators is crucial before final data enrollment.