**1. Introduction**

Given the frequency and the intensity of healthcare-related incidents, Artificial intelligence (AI) applications and cybersecurity threats in healthcare are all the rage now [1]. Cybersecurity is the process of protecting computer systems, networks, and programs from any unauthorized access. Cyberattacks have become more sophisticated using AI to get past cyber defenses. The AI is also being used to constantly manage and secure the increasing number of healthcare Internet of Things (IoT) sensor nodes and Cyber Physical Systems (CPS) devices as they connect and disconnect from hospital networks [2]. The CPS is intelligent system consisting of cyber and physical components which is controlled and monitored by AI algorithm. With the development of smart multisensory systems, sensorial media, smart things, and cloud technologies, "Smart healthcare" is getting notable attention from academia, government, industry, caregivers, and healthcare communities [3–9]. In the recent smart health technological revolution, IoT technology playing an important role in healthcare for it's ability to predict, prevent, and intelligently control the the emerging infectious diseas like, Coronavirus (Covid-19). Also, IoT has introduced the vision of a smarter world into a reality with large datasets and services [10–13]. The AI-driven IoT has become more popular in smart healthcare system by utilizing machine learning algorithms and by providing a better understanding of healthcare information to support improved personalized healthcare during the epidemic of Covid-19 [14–16]. Also, it can support powerful

processing and storage capacity of enormous datasets from IoT sensors and actuators as well as to provide automated decision making in real-time. A very little attention is given to developing a secure affordable healthcare system while the study of AI and cybersecurity for smart healthcare have been making great innovations in the age of Covid-19. The AI-driven IoT (AIIoT) for smart healthcare has the potential to revolutionize many aspects of our healthcare industry. AI-based analytics for secure smart health infrastructure is shown in **Figure 1**.

The importance of secure transformation in medical, public health, and healthcare delivery approaches have been recognized by numerous organizations [17]. The Networking and Information Technology Research and Development (NITRD) program recently has published the Federal Health Information Technology Research and Development Strategic Framework. This framework has explained the importance of the integration between the computing, engineering, mathematics and statistics, behavioral and social science, and public health research communities to explore the essential innovation to improve the services in the healthcare system [18]. Recent significant advances in machine learning (ML), artificial intelligence (AI), deep learning, high-performance cloud computing, and the availability of new datasets make such integration achievable.

Transformative approach can help to develop computational approaches for the analysis of multilevel and multiscale personal and clinical health data to maximize the accuracy of data implications. The transformative data science, mainly focuses on science and engineering innovations by interdisciplinary teams and utilize the advance sensing methods to intuitively and intelligently collect, connect, analyze and interpret data from individuals, device, and systems. Also, this integrated and intelligent data collection will help to optimize the healthcare services. The challenges include a number of issues from data collection, synchronization, fusion, and visualization of multisensory systems, electronic health records (EHRs), and medical and consumer devices. Underlying these challenges are many fundamentals issues, such as interoperability, integration, and reuse of heterogeneous data, feature selection, optimization, uncertainty quantification, robustness, model validation and evaluation, data privacy, and most importantly physical and cybersecurity. A robust research study might help to address how predictive, rigorous models with uncertainty can be build from sensory or EHR data for validation and testing and to improve the reproducibility of model building and simulations [18].

The World Health Organization (WHO) defines Smarthealthcare as "Information and Communication Technology applications in the healthcare, including disease

**Figure 1.** *AI-based Analytics for Secure Smart Health Infrastructure.*

*Smart Health and Cybersecurity in the Era of Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.97196*

control and monitoring, education, and research". Additionally, scientists state that "Smart Healthcare" is the integration of health informatics, public health, and business applications through the internet and related AI and data mining techniques. The above mentioned techniques can provide more security and high accuracy in personalized healthcare and health informatics. Though the deep learning concept becomes popular, the scientists have rarely used this technique to predict outcomes from multisensory health data. They prefer to make the healthcare prediction using algorithm based on statistical methods and regression analysis [19–21]. In this chapter, the authors discussed the importance and challenges of using AI for cybersecurity vulnerabilities that have compromised the confidentiality, integrity, and availability of data for the affected healthcare systems in the age of Covid-19.
