A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction

Year : 2025 | Volume : 14 | Issue : 03 | Page : 1 6
By

Priti Pal,

Munish Saini,

  1. Student, Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, Punjab, India
  2. Student, Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, Punjab, India

Abstract

This systematic study assesses recent developments in Machine Learning (ML) and Deep Learning (DL) approaches to predict pet diseases. With the increasing role of Artificial Intelligence (AI) in pet healthcare, this study identifies recent research trends, limitations, and future directions. A comprehensive search was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in selecting 20 relevant studies from over 300 articles published between 2020 and 2025. This analysis focuses on bacterial, viral, fungal, and zoonotic diseases in pets. The study covers real-world issues, model performance, dataset properties, and the interpretability of deployed AI systems. Future research is recommended to make use of sophisticated models like transformers, leveraging advanced models. This systematic study assesses recent developments in Machine Learning (ML) and Deep Learning (DL) approaches to predict pet diseases. With the increasing role of Artificial Intelligence (AI) in pet healthcare, this study identifies recent research trends, limitations, and future directions. A comprehensive search was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in selecting 20 relevant studies from over 300 articles published between 2020 and 2025. This analysis focuses on bacterial, viral, fungal, and zoonotic diseases in pets. The study covers real-world issues, model performance, dataset properties, and the interpretability of deployed AI systems. Future research is recommended to make use of sophisticated models like transformers, leveraging advanced models with improved feature extraction and explainable AI frameworks. Moreover, integration of multimodal data, such as medical imaging, genomic information, and clinical records, can enhance disease prediction accuracy. This study highlights the need for standardized datasets, ethical considerations, and user-friendly AI tools for effective adoption in veterinary practice.

Keywords: Artificial intelligence, pet disease, prediction, PRISMA, infectious diseases

[This article belongs to Research and Reviews : Journal of Veterinary Science and Technology ]

How to cite this article: Priti Pal, Munish Saini. A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction. Research and Reviews : Journal of Veterinary Science and Technology. 2025; 14(03):1-6.
How to cite this URL: Priti Pal, Munish Saini. A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction. Research and Reviews : Journal of Veterinary Science and Technology. 2025; 14(03):1-6. Available from: https://journals.stmjournals.com/rrjovst/article=2025/view=252676

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Regular Issue Subscription Original Research
Volume 14
Issue 03
Received 10/06/2025
Accepted 15/09/2025
Published 16/09/2025
Publication Time 98 Days

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