# Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review

## TL;DR

This paper reviews how AI can help diagnose breathing problems in dogs and cats by analyzing sounds, X-rays, and other data, but notes challenges like limited data sharing.

## Contribution

The study systematically evaluates recent AI applications in veterinary respiratory diagnostics and identifies key technical and practical barriers to adoption.

## Key findings

- AI models like CNNs and transformers show high accuracy in detecting conditions such as cardiomegaly and BOAS in pets.
- Data scarcity and lack of standardized datasets hinder broader implementation of AI in veterinary diagnostics.
- Multimodal approaches combining audio and imaging data offer promising results for respiratory disease detection.

## Abstract

Diagnosing breathing problems in dogs and cats is often difficult because traditional methods rely heavily on a veterinarian’s personal judgment and experience. This review examines how artificial intelligence—computer systems that can learn from data—can help to support the detection of these illnesses more reliably. We analyzed 24 recent studies where artificial intelligence (AI) was used in three ways: listening to breathing sounds, reading chest X-rays and scans, and combining different kinds of data like those from sound and movement sensors. The results show that AI can spot serious conditions like heart enlargement and lung diseases with high accuracy. However, wider use is limited by a lack of shared animal health data and real-world testing in clinics. Overall, AI offers great promise to support veterinarians in making quicker, more consistent diagnoses, leading to better care and healthier lives for pets.

Respiratory diseases represent a leading cause of veterinary consultations in dogs and cats, yet their detection remains challenging due to clinical variability and subjective interpretation of traditional diagnostic methods. In recent years, artificial intelligence (AI) has emerged as a promising tool to augment veterinary diagnostics through automated analysis of imaging and physiological data. This systematic review synthesizes and critically evaluates 24 studies published from 2019 onward that explore AI applications to support the detection of respiratory diseases in dogs and cats, focusing on three complementary modalities: audio-based (e.g., respiratory sounds), image-based (e.g., chest radiographs), and multimodal approaches. Our findings indicate that deep learning models, particularly convolutional neural networks (CNNs) and transformer architectures, achieve clinically relevant accuracy in detecting conditions such as cardiomegaly, alveolar patterns, and Brachycephalic Obstructive Airway Syndrome (BOAS). However, significant barriers remain, including data scarcity, lack of standardized datasets, and limited real-world validation. This review highlights the transformative potential of AI in veterinary respiratory diagnostics while underscoring the need for collaborative efforts in data sharing, methodological standardization, and clinical integration to realize its full impact in practice.