Year: 2025
Type: article
Abstract: The growing demand for smart pet healthcare systems has revealed the absence of AI-based platforms for preventive care. We present PAWMATE, an AI-integrated web platform that offers two essential functionalities: (1) image classification-based automatic detection of diseases, and (2) context-aware conversational chatbot answering veterinary-related questions. A Convolutional Neural Network (ResNet-50) is trained on pet datasets to categorize visible symptoms from pet images with an average accuracy of 89%. For real-time interaction, a Natural Language Processing (NLP) model—domain-specific intent fine-tuned—achieves 92.5% intent recognition accuracy and multi-turn dialogue support. Compared to conventional rule-based systems, our architecture improves responsiveness and decision support in pet care. Apart from application, the architecture can be generalized to other fields in which AI-based health diagnostic and advisory services are required. This paper presents the architecture, implementation, evaluation, and future generalizability of PAWMATE as a scientific contribution towards AI-based health systems.
Source: Journal of Scientific Advances
DOI: 10.63665/jsa.v2i2.05
Institution: MIT World Peace University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4413034309