{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T23:16:54Z","timestamp":1770679014280,"version":"3.49.0"},"reference-count":77,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T00:00:00Z","timestamp":1770422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad de Guayaquil","award":["FCI-021-2024"],"award-info":[{"award-number":["FCI-021-2024"]}]},{"name":"Universidad Bolivariana del Ecuador","award":["PROY-UBE-2024-019"],"award-info":[{"award-number":["PROY-UBE-2024-019"]}]},{"name":"Universidad Tecnol\u00f3gica Indoam\u00e9rica","award":["305.254.2022"],"award-info":[{"award-number":["305.254.2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Veterinary Sciences"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/vetsci13020163","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T08:15:54Z","timestamp":1770624954000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for the Diagnosis of Respiratory Diseases in Dogs and Cats: A Systematic Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6283-8197","authenticated-orcid":false,"given":"Franklin","family":"Parrales-Bravo","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Group, Universidad Bolivariana del Ecuador, Km 5 \u00bd v\u00eda Dur\u00e1n\u2014Yaguachi, Dur\u00e1n 092405, Ecuador"},{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial, Facultad de Ciencias Matem\u00e1ticas y F\u00edsicas, Universidad de Guayaquil, Guayaquil 090514, Ecuador"},{"name":"Faculty of Computer Science, Complutense University of Madrid, Av. S\u00e9neca, 2, 28040 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3616-2074","authenticated-orcid":false,"given":"Janio","family":"Jad\u00e1n-Guerrero","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n de Ciencias Humanas y de la Educaci\u00f3n (CICHE), Universidad Tecnol\u00f3gica Indoam\u00e9rica, Quito 170103, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9626-2341","authenticated-orcid":false,"given":"Katherine","family":"Medina-Castro","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial, Facultad de Ciencias Matem\u00e1ticas y F\u00edsicas, Universidad de Guayaquil, Guayaquil 090514, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0737-9132","authenticated-orcid":false,"given":"Rosangela","family":"Caicedo-Quiroz","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Group, Universidad Bolivariana del Ecuador, Km 5 \u00bd v\u00eda Dur\u00e1n\u2014Yaguachi, Dur\u00e1n 092405, Ecuador"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jatav, R.S., Pratap, A., Vaishnav, N., and Sharma, N. 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