International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Accessing timely preliminary medical guidance remains a critical challenge for individuals worldwide, with many relying on traditional healthcare systems that lack accessibility, personalization, and immediate response mechanisms. Current symptom checker applications provide limited diagnostic accuracy, lack real-time emergency detection capabilities, and fail to deliver comprehensive medical knowledge integrated with evidence-based treatment recommendations. The absence of intelligent, multi-agent healthcare systems leaves individuals underprepared to assess their health conditions, identify emergencies, and make informed decisions about seeking professional medical care.

To address these limitations, the MediAssist AI platform integrates Artificial Intelligence, Machine Learning, and Multi-Agent System architecture to deliver personalized, interactive preliminary medical guidance at scale. The system leverages advanced Natural Language Processing and machine learning classifiers to analyze symptom descriptions and predict likely medical conditions based on user input. Real-time emergency detection scans for critical keywords across eight life-threatening categories, ensuring immediate safety guidance delivery within 500 milliseconds. AI-powered knowledge agents retrieve comprehensive medical information from structured databases, while treatment recommendation agents provide evidence-based care guidance with appropriate medical disclaimers.

Through a user-centric web platform built with Streamlit and Flask, users describe symptoms naturally, receive instant emergency alerts when critical conditions are detected, view top-three condition predictions with confidence scores, access detailed medical knowledge about identified conditions, and obtain treatment recommendations with home care instructions. Stakeholders including general users, healthcare administrators, and medical professionals benefit from structured preliminary assessment workflows and comprehensive health literacy resources. By combining rule-based emergency triage with machine learning-powered symptom analysis and knowledge-driven medical guidance, the proposed solution significantly improves healthcare accessibility, reduces response time for critical situations, and enhances informed decision-making while democratizing access to preliminary medical consultation.

Downloads: | DOI: 10.17148/IJARCCE.2026.15167