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@article{196765,
        author = {Ch Rishitha and J. Jyothsna and P. Sireesha and G. Venugopal and Dr.U.Uma Maheswara Rao},
        title = {AI DRIVEN PERSONALIZED HEALTHCARE RECOMMENDATION SYSTEM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4684-4690},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196765},
        abstract = {This paper describes to help users manage their medial information; this project offers a full-stack healthcare application. The system analyses uploaded medical data using Google’s Generative AI (Gemini) to extract important information like possible conditions, prescription regimens, and doctor’s recommendations. An automatic email reminder system is then powered by these drugs, sending patients timely notifications according to the recommended frequency (e.g., once, twice, or thrice daily). Strong user management with role-based access restriction (patient/admin), safe authentication with JWT, and administrative tools for system supervision, such as user management and AI usage tracking, are all included in the platform. MongoDB is used for data durability, and pinecone is used for possible vector search features. To ensure system dependability and maintainability, a number of utility scripts support database seeding, report re-indexing, email testing, and API model validation.},
        keywords = {Healthcare, Full stack, Rule-Based, AI, Recommendation System, Web Application, Alerts, Remainders.},
        month = {April},
        }