Drug Recommendation System In Medical Emergencies

Abstract

In recent years, the convergence of artificial intelligence (AI) and healthcare has unlocked transformative possibilities for personalized patient care. This study presents a Drug Recommendation System that employs a transformer-based natural language processing (NLP) model to deliver medication suggestions based on a user’s reported symptoms, medical history, and profile data.The system features a robust Python backend powered by a fine-tuned ClinicalBERT transformer, coupled with a Next.js and TailwindCSS frontend that provides a modern, responsive, and engaging user experience. It processes patient inputs in real-time, detects potential drug interactions, and generates context-aware recommendations using trusted medical databases.Emphasis is placed on user safety, data privacy, and clinical reliability, making the system a valuable tool for both patients and healthcare professionals. This work demonstrates the potential of AI-driven platforms to support clinical decision-making by providing accurate, explainable, and proactive drug recommendations. By integrating deep learning and healthcare informatics into a responsive digital ecosystem, the system promotes safer, more personalized treatment adherence and empowers users in their healthcare journey.

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