Harnessing Artificial Intelligence for Disease Detection and Rapid Drug Discovery: A Path to Accelerated Medical Responses

Abstract

The history of Artificial Intelligence (AI) in drug discovery spans decades, from rule-based systems to sophisticated machine learning and deep learning algorithms. Early applications included virtual screening and QSAR modeling, which paved the way for data-driven drug development. Today, systems like IBM Watson Health and DeepMind's AlphaFold are good at analyzing medical data, predicting molecular interactions, and accelerating the design of novel drugs. Yet in most AI solutions that already exist, they usually only solve the specific tasks rather than formulating a comprehensive framework in emerging disease management. This paper proposes the integration of disease symptom data, pathogen-level analysis, and treatment prediction via an AI-driven model about diseases with symptoms such as cold, cough, or fever. The system correlates new pathogens with stored datasets and identifies potential medicine combinations for rapid testing and refinement, thereby significantly reducing the timelines for drug development. Hence, this approach addresses the severe need for scalable, fast-response solutions in managing infectious diseases and future pandemics