Smart Systems for Industrial Applications. Группа авторов
of communication technologies in healthcare is given in Table 1.1.
1.2 AI-Driven Communication Technology in Healthcare
The development of communication technologies plays a significant role in the growth of healthcare as an industry. To quote a few advancements in healthcare are rapid growth in the number of patient records that are converted into electronic health record. The health record is a document that covers historical information about the patients. Technology in healthcare is not only introducing digital transformation; it becomes a trend in controlling in every aspect of healthcare. In recent trends, Artificial Intelligence (AI) also gains attention in the enhancement of various system modeling, processes, and ease of prediction [9]. Most of the human illness are identified and diagnosed through image processing and pattern recognition algorithms. In recent times, AI is used to enhance the accuracy of imaging tools. In this chapter, we provide a detailed discussion of the impact of AI and various communication technologies in healthcare.
1.2.1 Technologies Empowering in Healthcare
In this section, we provide an overview of recent AI technologies that are responsible for recent shifts in healthcare, as shown in Figure 1.2.
Figure 1.2 Impact of AI in different domains of healthcare.
1.2.2 AI in Diagnosis
In this section, we present the effect of AI in healthcare for diagnosis application with examples. A simple process of AI transformation is shown in Figure 1.3. Online-based application has been developed to ease the process and increases real-time availability and accessibility of health-related information. Online healthcare has set a new channel for data transfer between the patient and the health unit. Extracting and analyzing the health record is a challenging task, which is achieved by reliable AI algorithms. These algorithms can predict the disease by understanding the nature of the patient’s record. Deep learning–based risk scoring and stratification tools are successfully developed to identify probable correlation from an unknown dataset within the patient’s record.
Application of AI in healthcare is utilized for acquiring a huge amount of data, processing the complex inheritance in them, and supporting decisions in case of the limited human intervention [10]. AI’s processing capabilities overcome the limitations mentioned above in healthcare and new methods to help doctors. AI is mainly used in diagnosing the illness compared with prognosis and therapy. Diagnosis is the process of observing and testing the patient, collecting information, analyzing the data, and finally providing a treatment plan. Diagnosis in AI is achieved by feeding patient information to the computing system, which produces diagnosis output.
Figure 1.3 AI-based diagnosis process.
1.2.3 Conversion Protocols
In this section, we discuss two basic protocols used for transforming the process into AI-based [11].
1 (i) Expert system
2 (ii) ANN-based model
The expert system is a technique that is followed for transforming the conventional diagnosis process into an AI-assisted. It outlines the step by step process involved from input to output by framing conditional protocols. The protocols are developed in domain wise by the clinician’s experience and knowledge. Artificial Neural Networks (ANNs) are a parallel processing technique or model which consists of neurons as processing elements, wherein neurons are linked and arranged layer-wise. Layers are also connected. The neurons are assigned with a functional unit and weight value based on the type of application. Weights of the processing could be varied to reach the specified result by applying a backpropagation algorithm. The parallel processing capability of ANN makes it more convenient method for medical applications. ANN-based model is illustrated in Figure 1.4.
Other than a medical diagnosis, ANN is used for analyzing the radiographs in radiology. Gamma, CT, ultrasound, and MRI images are manipulated using ANN. The digitized inputs are given to ANN, processed by inner layers of ANN, and produce appropriate outputs. Orthopedic injuries are successfully identified by trained ANN. In cytology, ANN is used to classify or group the abnormal cells from the specimen cell. It has also been utilized for interpreting EEG and ECG signals. Accuracy of performance on waveform analysis, identification of abnormalities, and data interpretation by ANN is very much useful and reduces the human intervention.
Figure 1.4 ANN-based diagnosis model.
1.2.4 AI in Treatment Assistant
Surgeries are performed by robotic machines, which are controlled by doctors. Robotic surgery is preferred with less invasive techniques, which allow them to perform tasks with better accuracy and control. Mobile ECG monitors and wide usage of smart health watches are utilized for keeping track of and after the surgery. Robotic assistants are employed in elderly care for assisting them in daily routines.
Interfacing the human mind and technology has significant applications in healthcare. Brain-computer interfaces assisted by AI enable patients with neurological diseases to talk, move, and communicate with others. In this scenario, a combination of AI and interfaces decode the neural activities related to the movement of limbs, which further stimulate the indented action.
Immunotherapy is one of the promising cancer treatments. In this therapy, tumors are treated naturally with the human immune system. But a lesser number of patients are responding to these kinds of treatments. Currently, a precise machine learning algorithm is developed to classify and identify the capability of the natural body immune system to treat cancer. AI is also supporting to enable virtual biopsies in the field of radiology, which achieves harnessing image-based algorithms to characterize the patterns and genetic structures of tumors.
AI could reduce the impact on shortages of well-trained healthcare providers by handling over some diagnostic tasks which are allocated to humans. An AI imaging tool take X-rays for tuberculosis symptoms with better accuracy compared to the human. This algorithm is diversified, which considers the environmental factors influencing the disease.
1.2.5 AI in the Monitoring Process
Smartphones have provided numerous tools for patients, which are extremely useful for extracting, transferring, and processing health information. Machine learning algorithms develop mobile applications for analyzing child facial diseases. These algorithms detect unique features such as the child’s jawline, eye, and nose placement to identify the craniofacial abnormalities.
Tracking health information is extended away from hospitals through wearable devices to maintain the fitness of the patients. Wearable devices are made up of internal sensors to measure and save health parameters. AI is used for extracting and analyzing the vast data from the devices