# Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems
###### Abstract
While this research concentrates on European aviation education, global integration in air transportation merits consideration of trends worldwide. In 2022, the Federal Aviation Administration (FAA) introduced a new course for certification in the Technical Discipline: Artificial Intelligence--Machine Learning [46]. This course aims to maintain and expand U.S. leadership in research and knowledge concerning the use of machine learning in aviation systems and the integration of machine learning-based components with aircraft software. In 2018, The International Air Transport Association (IATA) released a white paper, AI in Aviation: Exploring the Fundamentals, Threats, and Opportunities of Artificial Intelligence in the Aviation Industry [47]. IATA expressed a commitment to raising awareness and supporting airlines and the broader value chain to harness the benefits of AI through applied research, proof of concepts, pilots, and collaborations with academia and startups, thereby accelerating innovation. These efforts by civil aviation authorities to prepare for AI align with strategies at the highest levels of European governance. The European Commission's 2021 European AI strategy [48] (EU AI Act: the first regulation on artificial intelligence) aims to position the EU as a global leader in human-centric, trustworthy AI across sectors, including transportation. This indicates a policy-level recognition of AI's transformative potential and the necessity of creating an ethical, excellence-oriented framework to realize its benefits while ensuring safety. In April 2021, the EU Commission presented its AI package, including the following  
* Communication on fostering a European approach to AI [49];
* A review of the Coordinated Plan on Artificial Intelligence (with EU Member States) [50];
* A proposal for a regulation establishing harmonized rules on AI (AI Act) and relevant impact assessment [51]. While existing research has explored IoT and AI applications in aviation separately, the integration of these technologies as AloT for comprehensive health monitoring systems remains underexplored. This study aims to address this gap by proposing a holistic AIoT framework tailored for aviation health monitoring, examining how AIoT can shift the paradigm from reactive maintenance to proactive health management in aviation, and presenting a comprehensive model demonstrating the transition from traditional health monitoring to AIoT-enabled health management. By addressing these objectives, this study provides a comprehensive understanding of AIoT's potential in transforming aviation health monitoring systems, paving the way for safer, more efficient, and more sustainable air transportation. This paper is structured as follows. Section 2 outlines the materials and methods used in this study, including the development of the AloT-based aircraft health management model. Section 3 presents the results, detailing the proposed AIoT framework for aviation health monitoring and its various implementations. Section 4 discusses the implications of the findings, comparing traditional and AIoT-based health monitoring models and exploring the paradigm shift from health monitoring to health management. This section also considers future research directions in AIoT-enhanced aviation health monitoring. Section 5 concludes this paper, summarizing the key findings and their significance for the aviation industry.