Medication errors and adverse drug interactions have become major challenges in modern healthcare systems, often leading to serious health complications and increased burden on medical professionals. Traditional methods of checking drug compatibility are time-consuming and highly dependent on manual verification, which may result in inaccurate decisions during emergency or routine treatment processes. To address these challenges, this research proposes an AI-Powered Drug Information and Drug Interaction Management System designed to provide accurate drug-related information and real-time interaction analysis.

The proposed system integrates Artificial Intelligence, Natural Language Processing (NLP), and machine learning techniques to identify medicines, analyze drug combinations, and generate instant alerts for potentially harmful interactions. The system also provides personalized recommendations and alternative medicine suggestions to support safer and more effective treatment decisions. A user-friendly interface ensures accessibility for healthcare professionals as well as patients, including users from rural or non-technical backgrounds.

 

The architecture of the system combines a Flask-based backend, React/Flutter frontend, and a structured drug database to enable fast and scalable healthcare assistance. By automating the interaction-checking process and reducing dependency on manual verification, the proposed solution aims to minimize medication errors, improve patient safety, and enhance overall healthcare efficiency. The research highlights the potential of AI-driven healthcare systems in supporting smarter clinical decision-making and promoting safer medical practices.

Keywords

Artificial Intelligence, Drug Interaction Detection, Smart Healthcare System, Machine Learning, Natural Language Processing (NLP), Drug Information Management, Medication Safety, Healthcare Automation, Clinical Decision Support System, Real-Time Alert System