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@article{175636,
        author = {Nishikant Raut and Fatima A. Ansari and Vivek Chouhan and Rehan Sayyed and Rohit Deshmukh},
        title = {Medical Report Processing Pipeline using Llama 3.3 70b, Celery and Redis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4150-4153},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175636},
        abstract = {Efficient processing of medical reports enhances clinical decision-making and patient engagement. This system uses Llama 3.3 70b with Celery to asynchronously extract key health indicators such as hemoglobin, PCV, RBC, and leukocyte levels and etc. from uploaded reports. Reports are stored on AWS S3, and tasks are managed via Redis and Celery workers for scalable processing. Extracted data is visualized through clear, interactive charts, helping patients and doctors track health trends. A personalized chatbot is generated using the user’s last five reports, enabling meaningful, context-aware conversations. The system integrates PostgreSQL for metadata, MongoDB for chatbot memory, and Twilio for real-time status updates, ensuring fast, reliable, and user-friendly healthcare data interaction.},
        keywords = {AI, Celery, Redis, Health, Reports, Chatbot, Charts, Twilio, MongoDB, AWS, PostgreSQL},
        month = {April},
        }