## 7 Conclusions  
The adoption of noteworthy catalyzers of innovations, such as AI, blockchain, IoT, and cloud computing, will reshape the future of healthcare systems. However, the integration of these innovations of healthcare ecosystems comes with associated security threats, including the manipulation of the patient monitor, exploitation of the healthcare data repository, and interception of the communication between the healthcare provider and the patient, all of which can jeopardize healthcare operations. Therefore, there is a need to study different security challenges associated with smart healthcare systems along with their security countermeasures. Hence, in this study, we first reviewed emerging technologies and frameworks that offer automation, quality-of-service, fault tolerance, and intelligent healthcare functionalities to patients. Then, we explored the various security and privacy challenges facing the smart healthcare system, such as DoS, MiTM, data integrity attacks, phishing, and hardware-based attacks. Further, based on the security challenges of the healthcare industry, we reviewed prominent security solutions intended to strengthen the security and privacy of smart healthcare systems. Another contribution to this study is our proposal of an AI and blockchain-based secure architecture (as a case study) that analyses the malware and network attacks on the smart healthcare system. First, medical data are acquired from the data layer, which consists of different healthcare providers and patients. Then, dynamic malware analysis is used to remove the data associated with the malware by analysing its different characteristics, such as DLL, file size, hidden strings, and signatures. Further, a standard dataset is used to train AI models for network-related attacks in smart healthcare systems. The data are first preprocessed using data preprocessing steps, such as the insertion of missing values, data normalization, and datatype casting. Then, the preprocessed data are forwarded to the different AI models, such as RF, NB, LR, and perceptron. The RF outperforms other existing AI algorithms in termsof accuracy, i.e., 93.14%. Further, the non-malicious data (classified from AI models) are passed to the blockchain layer for secure data storage from data integrity attacks. Then, the proposed architecture is evaluated using performance parameters, such as blockchain scalability, accuracy, and dynamic malware analysis. Lastly, we discussed open issues and research challenges associated with smart healthcare systems in order to encourage other researchers and youngsters to offer better security solutions.  
In future work, we intend to strengthen the security and privacy of AI and blockchain-based smart healthcare systems by considering the various security attacks such as rowhammer, buffer overflow, masquerade, clone phishing, and phone phishing attacks, and the mechanism to tackle the aforementioned security attacks to further maintain the security of smart healthcare systems.  
Conceptualization, A.A.; Data curation, A.A. and I.K.; Funding acquisition, A.A.; Investigation, A.A.; Software, A.A. and I.K.; visualization, M.S.R.; Writing--original draft, A.A.; Writing--review & editing, M.S.R. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge Qassim University, represented by the Deanship of Scientific Research, on the financial support for this research under the number (10118-ccc-2020-1-3-I) during the academic year 1441 AH/2020 AD.  
Not applicable.  
The authors declare no conflict of interest.