AI and IoT in Self-Injection Monitoring: A Future Perspective on Personalized Healthcare Systems

Abstract

Self-administered injections are essential in managing chronic diseases such as diabetes, multiple sclerosis, and hormonal disorders. However, human errors in dosage, adherence, and technique present significant challenges. This study proposes an AI-powered IoT-based self-injection monitoring system that ensures precise medication administration while providing real-time feedback to users. The system utilizes smart sensors, AI-driven predictive analytics, and cloud-based data storage to prevent dosage errors, track injection patterns, and offer personalized medication insights. The integration of machine learning, computer vision, and cloud computing in healthcare not only enhances patient compliance but also enables remote monitoring by medical professionals. This paper details the system architecture, workflow, benefits, and future directions of AI-integrated self-injection monitoring systems.