AVA: AI Veterinary Assistance - An NLP and Semantic Vector-Based Clinical Decision Support System for Animal Healthcare

Abstract

Quick and correct veterinary assessment is a major problem for pet owners who often find it hard to tell the difference between minor health issues and dangerous emergencies. Traditional symptom checking methods depend on hospital-based clinical visits, which take a lot of time, need specific institutions, and are not available outside working hours. This paper presents AVA (AI Veterinary Assistance), a smart, NLP-based clinical decision support system for basic veterinary assessment. The system handles unstructured natural language symptom descriptions using a two-part prediction design that includes a Lexical Heuristic Matcher and a Semantic Vector Engine built on the all-MiniLM-L6-v2 SentenceTransformer model. AVA pulls out structured patient profiles from free-form text, links symptoms to a carefully collected MongoDB disease database of over 205 conditions, creates relevant follow-up questions, and provides ranked possible diagnoses with confidence scores and urgency levels. Testing results show a macro-average AUC of 0.988 and strong disease classification performance across multiple veterinary categories. The system is built as an interactive Streamlit web application with multi-language support, voice input through Whisper ASR, and optional skin lesion image analysis. AVA offers a scalable, easy-to-use, and clear AI-powered framework for helping pet owners and veterinary professionals in basic clinical assessment.

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