# Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
## Internet of Medical Things
### Artificial Intelligence  
Blockchain technologies get increasingly powerful and robust, as they become coupled with AI in various real-word healthcare solutions [162]. Machine learning and deep learning are the main driving ingredients for AI domain, which is also relentlessly improving the advancement of automation. The more data we feed to the machine, the more the ability a machine will gain to classify or predict patterns accurately [163]. [PERSON] and [PERSON] [164] introduced ModelChain framework based on blockchain technology. This framework utilizes a private blockchain to enable multiple institutions to contribute health data to train a machine-learning model improve care without disclosing their health records [164]. Such a framework increases the security and robustness of the distributed privacy-preserving health care predictive modeling across multiple institutions. [PERSON] et al. [165] proposed a blockchain based parallel healthcare system (PHS), including artificial systems, computational experiments and parallel execution (ACP). The ACP focused mainly on representing a patient's diagnosis, condition, and treatment process [165]. A consortium blockchain is added with the PHS to link hospitals, patients, health governing body and related communities [165]. The summary of benefits and limitations of the IoMT in blockchain technology is shown in Table 3.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Article** & **Blockchain Technology** & **Type of Data** & **Metis** & **Limitations** \\ \hline
[144] & \#Elevenum platform. & Sensor data & \# Integration of WBAN using smart contracts for securely automated patient monitoring. & \# Inefficient data ingestion. \\  & \# Public blockchain & & \\ \# Enhereum and Hyperledger platform. & Multimedia lof data & \# Dyslexia diagnosis data can be shared securely with mobile medical practitioners. & \# High upload time. \\  & \# Ethereum platform. & Sensor data & \# Ensuring transparency, data security, and data storage & \# Security risks for real-time monitoring because of faster block-time. \\  & \# Private blockchain. & & \\ \# Hyperledger Fabric platform. & EHR and sensor data & \# Robust against network fault such as distributed node down. & \# Vulnerable to attack. \\  & \# Public blockchain. & Sensor data & \# Ensuring anonymity and immutability. & \# Computational overhead is high. \\  & \# Public blockchain. & Sensor data & \# Log activity of entity and object. & \# Implementation of blockchain in such a large and wireless network. \\  & \# Public blockchain & & \\ \hline
[157] & \# Undefined. & EMR, EHR, and FHR Sensor data & \# Storage and mining overhead are reduced. & \# Vulnerable to security and privacy. \\  & \# Public blockchain. & & \# Reduced validation time by using Cluster Head. & \# Lack of transparency and trustworthiness. \\  & \# Private blockchain. & & \# Enhanced privacy in medical health prediction model. & \# Vulnerable to attack [166]. \\  & \# Proof of stake. & & \# Artificial healthcare systems. & \# Limited treatment scenarios are included. \\  & \# Private blockchain. & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the internet of medical things mechanisms for healthcare in the blockchain.