Abstract
Veterinary pathology is a critical field in veterinary medicine, necessary for the diagnosis of diseases in animals. However, it is faced with numerous challenges that impact the accuracy and efficiency of diagnoses. This manuscript explores into the multifaceted issues within this field, and emphasised on diagnostic challenges. Key areas of concern include the complexity of differentiating between similar pathological conditions, the limitations of current diagnostic tools and techniques, and the variability in the manifestation of diseases across different species. Furthermore, the manuscript highlights the shortage of specialised pathologists, which exacerbates diagnostic delays and errors. Advanced diagnostic technologies, such as molecular pathology and digital imaging, are explored for their potential to enhance diagnostic precision, yet their high cost and limited accessibility present significant barriers. The integration of artificial intelligence and machine learning in veterinary diagnostics is also discussed as a promising avenue to overcome some of these obstacles. Case studies illustrating common diagnostic pitfalls and their consequences on animal health are provided, underscoring the need for continuous education and training in this rapidly evolving field. In conclusion, there is need for a concerted effort to standardise diagnostic procedures and improve collaboration among veterinary pathologists, clinicians, and researchers. By addressing these challenges, the veterinary pathology community can improve diagnostic accuracy, ultimately enhancing animal health outcomes.