Severe equine asthma is a chronic respiratory disease of adult horses, occurring when genetically susceptible individuals are exposed to environmental aeroallergens. This results in airway inflammation, mucus accumulation and bronchial constriction. Although several studies aimed at evaluating the genetic and immune pathways associated with the disease, the results reported are inconsistent. Furthermore, the complexity and heterogeneity of this disease bears great similarity to what is described for human asthma. Currently available studies identified two chromosome regions (ECA13 and ECA15) and several genes associated with the disease. The inflammatory response appears to be mediated by T helper cells (Th1, Th2, Th17) and neutrophilic inflammation significantly contributes to the persistence of airway inflammatory status. This review evaluates the reported findings pertaining to the genetical and immunological background of severe equine asthma and reflects on their implications in the pathophysiology of the disease whilst discussing further areas of research interest aiming at advancing treatment and prognosis of affected individuals.

Artificial intelligence is playing an increasingly prominent role in veterinary diagnostics, offering innovative solutions to address the complexities of modern animal healthcare. This paper focuses on multimodal AI, a rapidly evolving field that integrates diverse data types, including medical imaging (e.g. X-rays, CT scans), textual data (e.g. clinical notes, lab reports), behavioral videos (e.g. gait analysis), and audio recordings (e.g. respiratory sounds). By combining these modalities, multimodal AI systems provide a holistic and data-driven approach to veterinary diagnostics, enhancing precision and efficiency. However, unlike human medicine, veterinary diagnostics must account for species-specific physiological variations, diverse movement patterns, and the absence of verbal patient feedback, making the integration of multimodal data both uniquely challenging and critically important. The objectives of this review are to consolidate current knowledge on multimodal AI applications in veterinary medicine while drawing insights from analogous applications in human medicine, identifying key challenges and knowledge gaps, and proposing future directions for research and clinical adoption. Key findings highlight the prevalent methods, including machine learning models, as well as advanced data fusion strategies for integrating multimodal inputs. The paper also addresses significant challenges, such as data collection and standardization, computational limitations, adoption barriers, and ethical concerns regarding data privacy and bias. The review underscores the transformative potential of multimodal AI in veterinary practice, with implications for improving diagnostic accuracy, reducing cognitive load, and streamlining clinical workflows. Moving forward, collaborative efforts between researchers, veterinary professionals, and technology developers are essential to overcome current barriers. With continued advancements, multimodal AI is set to become a standard tool, driving the future of veterinary diagnostics toward more comprehensive and efficient care.

Decision making consists of gathering quality data in order to correctly assess a situation and determine the best course of action. This process is a fundamental part of medicine and is what enables practitioners to accurately diagnose diseases and select appropriate treatment protocols. Despite severe equine asthma (SEA) being a highly prevalent lower respiratory disease amongst equids, clinicians still struggle with the optimization of routine diagnostic procedures. The use of several ancillary diagnostic tests has been reported for disease identification and monitoring, but many are only suitable for research purposes or lack practicality for everyday use. The aim of this paper is to assist the equine veterinarian in the process of decision making associated with managing SEA-affected patients. This review will focus on disease diagnosis and monitoring, while also presenting a flow-chart which includes the basic data that the clinician must obtain in order to accurately identify severely asthmatic horses in their everyday routine practice. It is important to note that European and American board-certified specialists on equine internal medicine can provide assistance in the diagnosis and treatment plan of SEA-affected horses.