AI-Powered Healthcare Assistant with Drug Interaction Detection and Emergency Support
Abstract
Modern healthcare delivery continues to be con-strained by fragmented digital tools, delayed diagnosis, unmoni-tored drug-drug interactions (DDIs), and insufficient emergency response mechanisms, particularly for users without direct spe-cialist access. This paper presents an AI-Powered Healthcare Assistant—an integrated, secure, web-based platform that unifies conversational symptom analysis, hybrid disease-risk classifi-cation, knowledge-graph-based drug interaction detection, and geolocation-enabled emergency support within a single appli-cation. The system combines a GPT-based NLP pipeline for symptom interpretation, a hybrid rule-and-scoring risk engine classifying patient state into Low, Medium, High, and Emergency tiers, and a Neo4j biomedical knowledge graph modelling drugs, diseases, genes, and proteins to flag Mild, Moderate, or Severe medication interactions. Built on React.js, Node.js/Express.js, MongoDB, and Neo4j, and secured through JWT authentication, bcrypt password hashing, and role-based access control (RBAC), the platform achieved a validated disease-prediction accuracy of 87.3% and end-to-end response latency suitable for interactive clinical decision support. Grounded in an evaluation spanning healthcare microservices, explainable AI, clinical NLP, graph neural networks, and healthcare data security, the system demonstrates that a single, well-secured platform can meaningfully bridge the gap between AI-driven diagnostic guidance and actionable, real-time patient care