Introduction: Early recognition of diseases in pets is essential, yet owners often face challenges in interpreting clinical symptoms. Digital symptom checkers offer a promising approach to encode veterinary knowledge, but their reliability and diagnostic accuracy remain largely unvalidated. This study addresses this gap through a method validation of a expert-knowledge-based veterinary symptom checker using synthetically generated test cases, enabling systematic exploration of the symptom-disease space in the absence of clinical data.

Methods: System performance was quantified using simulated user-checker dialogs across ≈550 diseases for dogs and cats, respectively. Robustness and efficiency were evaluated through three research questions: convergence probability, convergence speed, and structural factors influencing convergence.

Results: The system achieved full convergence under ideal conditions (100%), with rapid convergence (mean rank of one after ≈20 questions) and short response times (0.213-0.258 msec per disease). Under probabilistic user-answering strategies, performance decreased slightly but remained robust, with non-converging cases rare and correct diagnoses typically among top-ranked results (ranks 1-6 for dogs; 1-4 for cats). Structural analysis identified the number and uniqueness of symptoms as key predictors of diagnostic difficulty, with significant variation across anatomical regions.

Discussion: Findings confirm the system's internal consistency, robustness, and computational efficiency, establishing a validated foundation for evidence-based veterinary diagnostic support. Future work will include clinical and user studies to confirm performance under authentic conditions and address current limitations of synthetic data.