# Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
## Internet of Medical Things
### Healthcare IoT Infrastructure and Data Security  
Healthcare Information and Communication Technology (ICT) infrastructure consists of many networks and IoT devices, including terminals, sensor, diagnostic tools, wireless access points, etc. This infrastructure enables many healthcare systems such as wearable devices and remote monitoring services to transmit and share data about the patient's critical conditions with caregivers. During the transmission process, data traverse through unknown communication networks (e.g., Wide Area Networks) which might be vulnerable to security and privacy breaches. Indeed, there are many cases (e.g., Three Phishing attacks costs 20,000 patient's personal data breach at Catwaba Valley Medical Center in North Carolina [148]) where malicious attackers are relentlessly trying to discover any acute weakness to penetrate the healthcare network and compromise the service provider's valuable information. To address the above-mentioned security challenges in healthcare IoT, researchers are exploring various techniques to protect users' data from unlawful penetration. For example, [PERSON] et al. in [149] proposed a software defined network which virtualizes the traditional network infrastructure into several abstraction levels to build a resistance mechanism against malicious attack. The authors also suggested a fog computing architecture that assesses vulnerabilities of existing and newly added hardware into the healthcare IoT using OpenVAS framework. The main shortcoming of the proposed model is that the average assessment period of a single device is around 7 min and 21 s, which is too long and raises serious concern regarding the scalability of the system. This issue could be addressed by using blockchain-based Ethereum platform since block mining time is 10-15 s. Other research by [PERSON] and [PERSON] in [150] proposed a different solution based on application hardening such as code obfuscation and application program interface(APIs) to protect key logic and mobile applications, respectively. The study suggests the addition of a defense layer against reverse engineering by return oriented programming and check summing techniques. Although the security protection of implanted medical devices gadgets is well assisted in this study, heterogeneous technologies are required to integrate for a cumulative efficiency, but at the expense of increased complexity to the whole system. In order to enhance the Intrusion Detection Systems (IDSs) in IoT networks, the authors in [151] introduced Adaptively Supervised and Clustered Hybrid (ASCH-IDS) technique which classifies potential intruders from aggregated sensory data. A dissimilar proportion of aggregated sensory traffic is fed into two detection subsystems: Anomaly detection (ADSs) and Misuse detection (MDSs) for better performance. Interestingly, when the proportion adjustment increases the system performs well, and it shows the highest accuracy in terms of Sensitivity and Specificity when sensory data proportion on misuse detection is 0:25%. This process indicates an increased detected rate. Furthermore, the same authors in [152] employ Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), a deep learning-based methodology to monitor critical infrastructure for potential intruders using three hidden layers which shows increased accuracy of 99.91% compared to 99.80% in the previous study. However, the overhead cost of this new experiment is double the previously proposed model. As the self-driving autonomous vehicle is shaping the current transportation system gradually, its use in healthcare is indispensable, especially, in the healthcare SCM. [PERSON] et al. [153] introduced an intrusion detection mechanism to resist attack such as Denial of Service, Probe, Remote to user, etc. The authors proposed deep belief network for data dimension reduction, and decision tree using ID3 algorithm for intrusion classification. The detection accuracy of the systems is 99.92% which is quite high; however, the false negative rate is 1.53%.