**3. Artificial intelligence**

#### **3.1 How artificial intelligence helps in healthcare security and cybersecurity**

Artificial intelligence (AI) can provide a device or software program the ability to interpret complex data, including images, video text, and speech, or other sounds and to work on that interpretation to achieve the goal. Since AI-driven computers are programmed to make decisions with little human intervention, some wonder if

machines will soon make the difficult decisions we now entrust to our doctors. It is important to separate fact from science fiction, because AI is already here and it is fundamentally changing medicine, according to David B. Agus, MD, a professor of medicine and engineering at the University of Southern California Keck School of Medicine and Viterbi School of Engineering.

AI has been employed in applications in various domains of healthcare including cancer research, cardiology, diabetes, mental health, identification of Alzheimer's disease, stroke-related studies, identification of cardiovascular disease, etc. Rather than robotics, AI in healthcare mainly refers to doctors and hospitals accessing vast data sets of potentially life-saving information. The recent advancement of computing power can analyze the different features from the multisensory data for predictive analytics to identify the potential health outcomes through the machine learning techniques. The artificial intelligence and machine learning techniques use statistical methods to analyze incoming sensory and network data to identify patterns and security threat and make a decision with a minimum human interaction.

#### **3.2 AI in mobile heath (m-Health)**

Mobile health (m-Health) is the employment of smartphones and mobile devices with their communication to assist healthcare. M-Health comprises a combination of mobile devices, medical sensors, and smartphones. There is plenty of research that has shown that the application of AI in healthcare systems can significantly improve the security of patient health analysis. Like, the author in [29] proposed an AI-based smartphone application for predicting heart failures and alert the users. Currently, the researchers and healthcare providers are use and apply the simple methods for generating alerts in case of emergency. But, there are a high number of false alerts generated in the present methodology. The authors of this work used predictive models to avoid the impact of these false alerts. The proposed predictive models built based on the 44 months clinical data collected from 242 patients' smartphone who had experienced a heart failure at least once. In this work, the best predictive model developed using an application of a Naïve Bayes Classifier based on integration of observing data and a set of questions from the various alerts. The author claimed that their proposed model can lower the yearly rate of false alerts for a heart patient from 28.64 to 7.8 gradually.

Another m-Health based approach for speech recognition of users who are affected with dysarthria proposed in [30]. In this work, the author showed that their approach can assist in the process of voice message generation. The Hidden Markov Model approach was employed to measure the overall proximity of a word used in a speech model and is personalized for a particular user. The Hidden Markov Models are used to build AI to estimate the unknown parameters in a mobile target moving in a define environment. The speech recognition accuracy of their methodology is only 67% based on the real life study of nine test subjects. The authors of this work showed that the difficulties in the process of communication with users decreased significantly by using their proposed technology compared to the already available methods in the market. The drawback of this approach is the lower accuracy in speech recognition hardware and need usual aid for the voiceoutput communication.

#### **3.3 Internet of Things (IoT) and Cyber-Physical System (CPS) in the era of AI**

Healthcare systems in hospitals/clinics are one of the key targets of attackers for carrying out Internet-of-Things (IoT) and Cyber-physical System (CPS)-focused cyberattacks. The most critical endpoints from the hospital security viewpoint are

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

**Figure 3.**

*AI for Smart m-Health (the workflow with IoT and CPS communicate with a smartphone via Wi-Fi or Bluetooth).*

patient health monitoring, ventilation, anesthesia, infusion pumps, etc. There is increasing use of IoT in healthcare settings, including mobile devices, wearables, robots, drones, and contactless devices. IoT is enabling the control of coronavirus.

Early detection of Covid-19, isolation of infected people, and tracing possible contacts are critical to stopping the spread of the virus. IoT and CPS protocols, GPS, and Wi-Fi are providing solutions to the challenges that distance and accessibility would have posed. Using the IoT to fight virus outbreaks has been effective during Covid-19. Interconnected tech devices, such as smart thermometers to test a patient's temperature, are used to build up detailed datasets for more accurate analysis and diagnosis. Quarantine compliance is also greatly assisted by the use of IoT. By using a patient's existing smartphone or wearable devices, it is easier to ensure compliance with quarantine rules and establish patterns via track-and-trace methods.

A Cyber-Physical System (CPS) is a collection of sensors/devices interacting with each other and communicating with the physical world. Many CPS application is based on the medical devices used in smart healthcare technology. Advances in CPS will enable capability, adaptability, scalability, resiliency, safety, security, and useability that will expand the horizon of critical application in the healthcare system with cybersecurity. The ideas in CPS-based research are being challenged by the new research concepts emerging from AI and machine learning. The integration of AI with CPS especially with real-time secure health care operation creates new research opportunities with major societal implications. The application of AI and smart m-Health with the workflow including IoT and CPS communicate with a smartphone via Bluetooth or Wi-Fi is shown in **Figure 3**.
