A Review of Multimodal AI in Veterinary Diagnosis: Current Trends, Challenges, and Future Directions

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.