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Toward collaborative artificial intelligence development for animal well-being.

Jennifer J Sun

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    Summary
    This summary is machine-generated.

    Future artificial intelligence (AI) in veterinary medicine offers opportunities for animal behavior analysis. Collaboration between AI researchers and veterinarians is crucial to address challenges in data, modeling, and evaluation for improved animal welfare.

    Keywords:
    AI for veterinary medicineanimal behavioranimal well-beingartificial intelligencefoundation models

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    Area of Science:

    • Veterinary Medicine
    • Computer Science
    • Artificial Intelligence

    Background:

    • AI development for animal behavior analysis is rapidly advancing.
    • Current AI paradigms include supervised learning, self-supervised learning, and foundation models.
    • These technologies present unique opportunities and challenges within veterinary medicine.

    Purpose of the Study:

    • To review the opportunities and challenges of AI in veterinary medicine.
    • To examine AI paradigms for automating animal behavior analysis.
    • To advocate for a collaborative approach to AI development in veterinary practice.

    Main Methods:

    • Review of AI paradigms: supervised learning, self-supervised learning, and foundation models.
    • Analysis of AI applications and limitations in animal behavior analysis.
    • Exploration of challenges in data, modeling, and evaluation for veterinary AI.

    Main Results:

    • AI offers significant potential for automating animal behavior analysis.
    • Limitations exist in data acquisition, model development, and evaluation metrics.
    • Cross-domain collaboration is essential for successful AI integration.

    Conclusions:

    • Future AI development in veterinary medicine requires a multidisciplinary approach.
    • Integrating AI expertise with veterinary knowledge will enhance animal behavior analysis.
    • Prioritizing human and animal well-being is key to advancing veterinary practice through AI.