Developing Trusted IoT Healthcare Information-Based AI and Blockchain

• Published in: Processes Vol. 11; no. 1; p. 34
• Main Authors: AlGhamdi, Rayed, Alassafi, Madini O., Alshdadi, Abdulrahman A., Dessouky, Mohamed M., Ramdan, Rabie A., Aboshosha, Bassam W.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Algorithms; Automobile industry; Blockchain; COVID-19; Cryptography; Deep learning; Fatalities; Flaw detection; Health care; Health care industry; Internet of Things; Machine learning; Medical personnel; Pandemics; Privacy; Security; Supply chains; System effectiveness; Transfer learning
• DOI: 10.3390/pr11010034
• ISSN: 2227-9717

The Internet of Things (IoT) has grown more pervasive in recent years. It makes it possible to describe the physical world in detail and interact with it in several different ways. Consequently, IoT has the potential to be involved in many different applications, including healthcare, supply chain, logistics, and the automotive sector. IoT-based smart healthcare systems have significantly increased the value of organizations that rely heavily on IoT infrastructures and solutions. In fact, with the recent COVID-19 pandemic, IoT played an important role in combating diseases. However, IoT devices are tiny, with limited capabilities. Therefore, IoT systems lack encryption, insufficient privacy protection, and subject to many attacks. Accordingly, IoT healthcare systems are extremely vulnerable to several security flaws that might result in more accurate, quick, and precise diagnoses. On the other hand, blockchain technology has been proven to be effective in many critical applications. Blockchain technology combined with IoT can greatly improve the healthcare industry’s efficiency, security, and transparency while opening new commercial choices. This paper is an extension of the current effort in the IoT smart healthcare systems. It has three main contributions, as follows: (1) it proposes a smart unsupervised medical clinic without medical staff interventions. It tries to provide safe and fast services confronting the pandemic without exposing medical staff to danger. (2) It proposes a deep learning algorithm for COVID-19 detection-based X-ray images; it utilizes the transfer learning (ResNet152) model. (3) The paper also presents a novel blockchain-based pharmaceutical system. The proposed algorithms and systems have proven to be effective and secure enough to be used in the healthcare environment.