## 1 Introduction
For instance, the authors of [5] explored the security solutions of [6] on mutual authentication and patient anonymity for telecare medical information systems. They improved the results of [6] by examining the session keys between the communication entities of the healthcare system. Their proposed framework effectively addresses the forgery and spoofing attacks. Further, [PERSON] et al. in [7] proposed an AI-based lightweight and secure communication scheme to tackle protocol-based security threats. First, the patient's sensor collects data and forwards it to the personal server attached to the access points. At the receiving side, the healthcare providers (intended recipients of the data) are associated with the trusted authority for  
Figure 1: Total financial loss due to cyberattacks on the healthcare industry (2006–2026) [3]. registration and authentication purposes. Then, the data are processed (i.e., prediction and classification using AI algorithms),and this step is overseen by the trusted authority. Their proposed scheme outperforms others in terms of delay, throughput, computation time, and accuracy with other baseline works. However, AI algorithms cannot deal with data integrity attacks, where an attacker intercepts the communication in order to fetch and inject the malicious code into the legitimate data. In addition, the attacker passively (covertly) listens to the network communication to find sensitive information about the healthcare resources, which includes the number of patients are in critical conditions, what the thresholds of implant devices are, and login credentials of patient information systems [8]. This information is crucial from the security perspective because if the information gets into the wrong hands, severe privacy-related issues can threaten the smart healthcare system. Hence, the researcher has adopted blockchain technology characterised by decentralization, immutability, transparency, and security [9; 10]. [PERSON] et al. in [11] explored the benefits of blockchain technology in the smart healthcare system, as it resolves the issue of data integrity, transparency, and security. They proposed a traceable system for electronic health records, whereby a patient and user can securely exchange their data. They used a private blockchain by implementing it in the HyperLedger Fabric, where two separate databases are maintained for data traceability. Further, the authors in [12] studied the heterogeneity problem of the smart healthcare system resulting from the variety of devices and data formats, which makes data management difficult. To address this issue, the authors presented a blockchain and cloud-based secure and efficient framework for better interoperability in smart healthcare systems. The integration of blockchain with cloud computing reduces the computational cost and increases the security and privacy of the smart healthcare system. [PERSON] et al. in [12] discusses the benefits of amalgamating blockchain and cloud technology to offer cost-effective and secure data storage of medical data. They proposed a five-tier secure and reliable framework for better interoperability between different healthcare providers. Next, [PERSON] et al. in [13] proposed an AI and blockchain-based secure approach to securely transmit securely a patient's medical data to the medical staff. Firstly, they applied machine learning models to classify malicious data obtained by wearables and remove them from the healthcare systems. Then, they used blockchain technology to store the non-malicious wearable data to confront data integrity attacks. Similarly, the authors of [14] presented a collaborative framework comprising a deep learning model, blockchain, and 6G network interface. In their proposed framework, the predicted data from the deep learning models is stored inside the public blockchain to offer secure data storage. Moreover, the communication between each entity of their proposed framework is provided by the 6G network that offers low latency communication.