Core Insights - The pet medical industry is facing challenges such as complex cases, tight diagnostic resources, and highly fragmented data due to the continuous growth in pet numbers. Traditional diagnostic methods relying on experience are insufficient to meet the demands for precision and efficiency from pet owners and medical institutions [1] Group 1: Intelligent Diagnosis Logic - The "Pet Life All Things" 4.0 version introduces a dynamic decision tree and symptom weight factor algorithm, shifting from fixed rules to interactive dynamic reasoning, which reduces false positive rates by 41.6% and rare disease misdiagnosis rates by 28.3% in simulated consultations [4] - The system can dynamically generate follow-up questions based on atypical symptoms, enhancing diagnostic efficiency and providing reliable assistance to veterinarians, making AI a true clinical decision-making partner [4] Group 2: Real Medical Data Support and Clinical Expertise - The AI model is backed by extensive real medical data, covering 12 million pet cases and collaborating with over 30,000 pet medical institutions, resulting in more than 68 million processed pet treatment records [5] - The data has a high standardization rate of 97.2%, ensuring quality in model training across over 30 medical specialties, with an 88.9% accuracy rate in identifying seven key monitored rare diseases as of Q2 2025 [5] - The model includes a drug recommendation system that integrates a veterinary drug knowledge graph and a banned drug exclusion database, covering over 12,000 legal veterinary drugs and 3,800 banned types, reducing hallucination content from 4.3% to 0.7% [5] Group 3: Handling Complex Cases and Multi-modal Diagnosis - The model supports 98K long context processing, allowing for continuous logical analysis across multiple symptoms and disease stages, maintaining the integrity of diagnostic logic chains [6] - In complex cases with four or more combined symptoms, the diagnostic accuracy improved from 72.1% to 89.4%, comparable to the average level of experienced veterinarians at 88.7% [7] - The AI image analysis sub-model has an average recognition accuracy of 93%, covering various directions such as urine abnormalities and skin lesions, with over 570,000 images reviewed by imaging experts [7] Group 4: Clinical Implementation and Intelligent Iteration - The company has integrated AI modules into clinical workflows in collaboration with leading pet hospitals, with over 80,000 real consultations utilizing the model daily and an adoption rate of over 83% from doctors [9] - A data closed-loop mechanism has been established to continuously optimize the model, ensuring knowledge graphs are updated quarterly based on B-end hospital data and C-end user feedback [9] - The company has secured over 60 million RMB in funding to support deep model training, knowledge graph expansion, and multi-language version development, positioning itself in the top tier of the domestic pet AI model sector [9]