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@article{206679,
        author = {Daya Naik and Varsha S and Shreyas G Kulkarni and Varshitha V},
        title = {MedWaste.AI: AI-Powered Biomedical Waste Classification System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {169-174},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206679},
        abstract = {This paper presents MedWaste.AI, an intelligent biomedical waste management platform that automates waste identification and delivers context-specific disposal guidance. Improper segregation of clinical waste continues to pose significant threats to public health and environmental safety, particularly within high-throughput healthcare facilities. The proposed system employs a deep learning-based object detection model to classify waste from images and supports text and voice-based queries to accommodate diverse user needs. Upon identification, the system retrieves relevant regulatory information through a Retrieval-Augmented Generation (RAG) pipeline and generates accurate, situation-specific disposal instructions. A text-to-speech output module further improves usability in time-critical clinical environments. By integrating computer vision, natural language processing, and speech technologies, MedWaste.AI demonstrates high classification accuracy, reduced manual workload, and improved compliance with established waste disposal standards.},
        keywords = {Artificial Intelligence, Biomedical Waste Management, Computer Vision, YOLOv8, Deep Learning, Retrieval-Augmented Generation, Speech Processing, gTTS.},
        month = {July},
        }