Hackathon Team 1 || DEDSEC || Presented by DJ Saghvi College || Shubham, Varad, Azlan

MedNourish AI, an innovative medical assistant leveraging the power of Retrieval-Augmented Generation (RAG) to transform healthcare diagnostics. This solution aims to overcome significant challenges in modern healthcare systems, providing both accuracy and personalization in medical support.
One major hurdle addressed is the reliance on outdated medical knowledge. With medical research and guidelines evolving rapidly, many diagnostic tools fail to keep pace. MedNourish AI solves this by integrating a Dual Knowledge Base Structure, combining a dynamic, comprehensive medical database with a personalized knowledge library. This ensures real-time updates aligned with the latest standards and offers responses tailored to individual medical histories.
The solution also tackles the risks of generic diagnostics and AI hallucinations by enhancing personal context awareness. By factoring in a patient’s unique historical data, MedNourish AI delivers nuanced and patient-specific insights, reducing the likelihood of misdiagnosis.
The presentation further elaborates on the system’s architecture, showcasing how RAG technology is seamlessly integrated to outperform traditional Large Language Models (LLMs). Comparative results demonstrate the system's efficacy, highlighting its ability to generate accurate, contextually appropriate responses where conventional LLMs fall short.
This groundbreaking work by Team DedSec signifies a major leap toward precision medicine, setting new benchmarks for AI in healthcare diagnostics.
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