Leveraging AI and ML in Rapid Saliva Drug Testing for Efficient Identification of Drug Users

Drug abuse remains a pervasive societal issue with far-reaching consequences for individuals and communities. Current drug testing methods often need more speed and accuracy for timely intervention. This proposal introduces an innovative approach to drug detection by integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into rapid saliva drug testing devices. By harnessing AI and ML capabilities, the proposed solution aims to enhance the efficiency and accuracy of drug detection while minimizing false positives and negatives. The device will be portable, user-friendly, and capable of delivering quick results within minutes, making it suitable for deployment in diverse settings such as workplaces, schools, law enforcement, and healthcare facilities. Through collaborative efforts with experts in AI, ML, and drug testing technology, the device will undergo rigorous development, validation, and regulatory approval processes. Upon implementation, it anticipated that the integration of AI and ML into rapid saliva drug testing would lead to improved public health outcomes by enabling early identification and intervention for individuals struggling with drug abuse. This abstract outlines the methodology, key features, implementation plan, expected outcomes, and potential impact of the proposed solution in addressing the challenge of drug abuse.