Artificial Intelligence in Smart Radiation Monitoring Devices: Enhancing Safety and Efficiency in Radiation Management

Abstract

Artificial Intelligence (AI) is transforming radiation monitoring by offering enhanced sensitivity, faster response times, and more adaptive management strategies across healthcare, nuclear safety, and environmental applications. This paper presents a comprehensive analysis of AI’s integration into smart radiation monitoring devices, based on an extensive review of recent academic literature. Using a systematic search methodology across major databases, the study identifies key technological advances, performance metrics, emerging research directions, and critical implementation challenges. Findings indicate that AI-driven systems improve detection accuracy from 75–80% in traditional systems to 90–95%, reduce anomaly response times from 10–15 minutes to 1–2 minutes, and lower false positive rates significantly. However, challenges related to data quality, model interpretability, system integration, and regulatory compliance remain pressing barriers. Addressing these issues requires multidisciplinary collaboration, standardized data protocols, and the development of transparent and robust AI models. The paper concludes that while AI holds immense promise for advancing radiation safety and operational efficiency, careful governance and continuous innovation are essential to realize its full potential in real-world applications.