Convolutional neural networks enabling the Internet of Medical Things: security implications, prospects, and challenges
Peace Busola Falola, Joseph Bamidele Awotunde, Abidemi Emmanuel Adeniyi, Agbotiname Lucky Imoize · 2024
The integration of Convolutional Neural Networks (CNNs) into the Internet of Medical Things (IoMT) heralds substantial advances in healthcare, including improved diagnostic precision, real-time data processing, and the possibility of individualized medication. However, this integration raises complicated security concerns, notably around privacy and the safeguarding of sensitive medical data. The capacity of CNNs to analyze large datasets raises concerns about data security, integrity, and availability, which are critical for retaining patient confidence and adhering to regulatory norms. Despite these obstacles, CNN-enabled IoMT has the potential to significantly improve healthcare outcomes through more accurate diagnosis and predictive analytics. However, attaining these advantages requires overcoming technological hurdles such as data variety and representation, processing needs, and the interpretability of AI-driven choices. This chapter emphasizes the importance of taking a balanced strategy that capitalizes on CNNs' revolutionary potential in the IoMT while thoroughly addressing the accompanying security issues.