Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare

Osteoporosis (OP), or porous bone, is a severe illness wherein an individual's bones weaken, increasing the likelihood of fractures. OP is caused by micro-architectural degradation of bone tissues, which raises the probability of bone fragility and can result in bone fractures even when no force is placed on it. Estimating bone mineral density (BMD) is a prevalent method for detecting OP. For women who have reached menopause, prompt and precise forecasts and preventative measures of OP are essential. BMD can be measured using imaging methods like Computed Tomography (CT) and Dual Energy X-ray Absorptiometry (DEXA/DXA). Blockchain (BC) is a revolutionary technique utilized in the health sector to store and share patient information between clinics, testing centres, dispensaries, and practitioners. The application of Blockchain could detect drastic and even serious errors. As an outcome, it may improve the confidentiality and accessibility of medical information interchange in the medical field. This system helps health organizations raise awareness and enhance the evaluation of health records. By integrating blockchain technology with machine learning algorithms, various bone ailments, including osteoporosis and osteoarthritis, can be identified earlier, which delivers a report regarding the prediction of fracture risk. The developed system can assist physicians and radiologists in making more rapid and better diagnoses of the affected ones. In this work, we developed a completely automated mechanism for suspicious osteoporosis patients that uses machine learning techniques to improve prognosis and precision via different processes. Here, we developed a computerized system that effectively integrates principal component analysis (PCA) with the weighted k-nearest neighbours algorithm (wkNN) to identify, predict, and classify the BMD scores as usual, osteopenia, and osteoporosis. The ranked results are validated with the DEXA scan results and by the clinicians to demonstrate the efficacy of the machine learning techniques. The laboratories use BC to safely and anonymously share the findings with the patients and doctors. 

This chapter enlightens the identification of anaemia due to malnutrition from the colour of the nail images using a smartphone application. This method enables remote measurements and monitoring using a noninvasive procedure. Since this method does not involve invasive techniques, there is no blood loss, and it is painless. In addition, the smartphone application facilitates easy measurements of various physiological parameters related to the blood. They include Hemoglobin (Hb), iron, folic acid, and Vitamin B12. This technique can be accomplished using a feed-forward neural network trained with a Radial Basis Function Network (R.B.F.N.). The image of the fingernails is photographed using a camera built into the smartphone. Online anaemia detection smartphone application will classify the anaemic and Vitamin B12 deficiencies as onset, medieval, and chronic stages by feature extraction from the nail images. The specific measurements made instantly can extract features like the colour and shape of the fingernails. These features train the R.B.F.N. to identify Anemia due to malnutrition. This method will enable the depreciation and disposal problems associated with bio-medical waste. Also, this method will offer a contactless online measurement scheme. The application could help in the early detection of Anemia due to malnutrition, allowing users to seek medical advice and intervention promptly. In terms of accessibility, by utilizing a smartphone application, this technology could reach a broad audience, including those in remote or underserved areas.

Regarding the privacy of medical images, Blockchain's encryption and decentralization would enhance data privacy and control for users. The data extracted from the nail images for research is obtained with the user's consent. Anonymized data could be used for research purposes, contributing to a better understanding of anaemia and malnutrition trends.

Recent developments in the fields of Artificial Intelligence (AI) and bioinformatics have played a vital role in securing smart healthcare. Notable contributions have been made in the field of viral immunology after the COVID-19 outbreak with the help of AI and bioinformatics. Various diseases and disorders such as viral diseases, metabolic disorders, and genetic disorders require the application of AI and bioinformatics to provide safe and error-free treatment. The tools of bioinformatics and modern-day biology used for smart and secure health care include single-cell genomics, proteomics, and next-generation sequencing technologies. During the COVID-19 outbreak, AI and bioinformatics helped to create methods and services to combat the pandemic. In this chapter, we elaborately highlight the principle, procedure, and applications of AI equipped with bioinformatics knowledge to create opportunities, and prospects and answer the challenges met by academicians, researchers, students, and industry professionals from the background of computer science, bioinformatics, and healthcare. 

Cancer is the second deadliest disease in the world. Breast cancer tops the list among the diseases affecting women. Specific strategies should be devised which will mitigate the effects of breast cancer. The risks can be mitigated if the detection takes place at an early stage. Early detection leads to improved outcomes, and survival remains a cornerstone of cancer control. Currently, mammograms are used to capture and observe the 2D nature of the tissues. 2D mammogram reports are used to train convolutional neural networks. 2D mammograms capture anterior and posterior images of the breast. These images, alone, are not sufficient to adjudicate whether the lump is benign or malign. Convolutional Neural Networks have attained great success in image classification, but they fail in some areas since they learn about the image statically. They do not take into consideration spatial information about the image and its subparts. There is no significant change reflected in the output if there is some alteration in the input. CNNs tend to lose lots of valuable information in the process of pooling. To overcome all these shortcomings, 3D data will be used to train the network, which captures all the orientations of the tissues. 3D mammograms, also known as tomosynthesis, are also very helpful for women who have concentrated dense tissues. Dense tissues make it difficult to locate the abnormalities. In addition to 3D data, clinical history, genomic information, and pathology reports have been taken into consideration. The amalgamation of the heterogenic data helps in the accuracy of the prediction because it will analyze all the contexts before arriving at a decision. Capsule neural networks have been used to overcome the drawbacks of convolutional neural networks. Convolutional neural networks require a lot of training data, which is not readily available. It takes a lot of time to train the model since the volume of data is huge. It is not capable of recognizing deformed objects in various orientations. Capsule Neural Network addresses all these issues and improves the performance reasonably.

A common cancer subtype found in women with high mortality and occurrence rates is Breast Cancer (BC). BC ranks second among the causes of high mortality rates in women. The annual death rate due to breast cancer surpasses that of any other cancer type. The global survival rate for patients with breast cancer remains suboptimal. To enhance this survival rate, it is essential to implement intervention techniques for early detection and treatment. Screening using the Medio-Latera- -Oblique (MLO) view and the Cranio-Caudal (CC) view improved the detection of cancer signs in small lesions. This motivated the radiologist to use both mammographic views for screening and subsequently to acquire additional information. To automate this sequential screening process, Image Processing, and Artificial Intelligence (AI) techniques are incorporated into these views individually and their results were fused. Further, feature fusion from both views is analyzed by researchers to enhance the overall performance of the system. The proposed model is more concentrated on the extraction and fusion of deep features from the two views to improve screening efficacy. The effectiveness of the proposed workflow is assessed on mammogram images taken from the MLO view and CC views of the DDSM dataset. Medical imaging data in conjunction with Machine Learning (ML) methods are employed for breast cancer (BC) detection and classification, but they tend to be time-intensive. Leveraging Deep Learning (DL) algorithms has the potential to further enhance the detection accuracy.

This work focuses on improving the detection performance by using a fusion of texture and Resnet 50 deep feature of MLO and CC view mammograms followed by Support Vector Machine (SVM) classification. An improved accuracy of 98.1% is achieved when compared to existing works. Henceforth, this work can be employed for the early BC diagnosis.

As medicine continuously evolves, recent advances such as Artificial Intelligence gain prominence for their potential role in enhancing routine clinical practice. One such application is its role in diagnostic colonoscopy to aid in the early detection of precancerous lesions and enable prompt management.

The area for communication and networking, as well as the area for the body, and the Service Delivery Area, are the three key components that make up Smart Healthcare. In addition to enhancing the quality of medical care delivered by remote monitoring, this technology has the potential to cut the cost of a variety of medical equipment while simultaneously boosting their operational efficacy. Connecting the Internet of Things with Big Data and cloud computing has the potential to deliver answers to a variety of urgent problems that occur in real time when these technologies are used in conjunction with intelligent apps for healthcare. Cloud computing offers a collaborative environment for working with the Internet of Things (IoT) and big data as a result of its many applications. Big data is in charge of the data analytics technology, while the Internet of Things is in charge of the data source. Both of these facets are managed by the Internet of Things. An overview of healthcare analytics in an environment made possible by the Internet of Things is presented in this chapter. Topics covered include the advantages, applications, and issues associated with this field. The applicability of the framework is evaluated by real-time analysis of data provided by patients for automated management of the patient’s blood sugar levels, body temperature, and blood pressure. Improvements have been made to the patient's health monitoring conditions as a direct consequence of the integration of the system. The technology notifies doctors and other medical professionals in real time about any changes that may have occurred in their health status to provide recommendations on preventative care. The efficiency of these kinds of systems is determined by the use of a wide range of technological approaches. In this study, we take a methodical look at the factors that led to the development of modern healthcare, including its origins, its methods, and its effects. An explanation of the chronological order of the procedures is provided. In the article, each stage of development is broken down and analyzed in terms of its social relevance, scientific and technical significance, communications significance, and application of information technology significance. A particular emphasis was placed on the technical component of the system, in particular, the application of network technologies and services, as well as the introduction of emerging technology that consists of numerous factors, and assists us in the process of monitoring a person’s status by providing us with useful information. Because of the widespread spread of COVID-19, health problems have emerged as a primary source of worry. A healthy population is required for the existence of a harmonious society. The foundation for a healthy society will be laid by forward-thinking healthcare in forwardthinking cities. Technology improvements in sensors and communication devices have resulted in the development of effective solutions in a variety of networking industries, public and private corporations, and government agencies throughout the world. In addition, the worldwide reach and efficiency of smart devices and mobile technologies have expanded thanks to the expansion of their use in the healthcare sector. Patient monitoring systems located at the bedside as well as patient monitoring systems located remotely are the two primary subtypes of patient monitoring systems that may be distinguished from one another. It is becoming more common for healthcare professionals to make use of such technology in clinical as well as non-clinical contexts. As a consequence, major advancements have been made in the field of healthcare. In a similar vein, untold numbers of normal operators benefit from MHealth (Mobile Health) and E-Health, both of which use information and communication technology to sustain and improve. Through the use of an ontologybased survey, the researchers expect to be able to follow the participants’ health over time and make suggestions for routine workouts. This project’s primary emphasis is placed on the creation of the findings of the MAX30100 sensor, the MLX sensor, and the digital BP sensor after they have been combined into a single kit, as well as on the integration of these three sensors into the kit. The results of the temperature, blood pressure, SpO2 , and heart rate monitoring are concurrently shown on the LCD and in the mobile app as normal or abnormal readings. The device is also capable of displaying a person’s overall health status. The comparison of all four threshold values brings in this result, which may either be normal or abnormal depending on the circumstances.

The most liked blockchain healthcare application at present is safeguarding our critical medical data. There are many security issues that the healthcare sector must deal with. Between July 2021 and June 2022, 692 significant healthcare data breaches were revealed. The thieves grabbed information from banks, credit cards, health data, and genomic tests. Data on the blockchain is incorruptible, decentralized, and transparent, which makes it perfect for security applications. Furthermore, blockchain protects the confidentiality of medical data by being transparent and private, hiding anyone’s identity with intricate and secure algorithms. Patients, medical professionals, and healthcare providers may simply and securely exchange the same information thanks to the technology’s decentralized nature. Blockchain applications enable the accurate identification of medical errors, including risky ones. Blockchain technology significantly contributes to the handling of fraud in clinical trials. In this case, the technology may increase data efficiency in the healthcare sector. By supporting a distinct data storage pattern, the system can aid in preventing data manipulation in the healthcare industry. It guarantees adaptability, connectivity, accountability, and data access authentication. The confidentiality and safety of health records are essential for different purposes. Healthcare data can be digitized and protected in a decentralized manner with blockchain technology.

Communication between people is the key to delivering a message. It is easier for normal people to have a communication medium (language) known between them. A person with speech impairment or hearing difficulty cannot communicate with others like a normal human. Sign language helps people with disabilities to communicate with each other. In sign language systems, there is no de facto standard followed by all the countries in the world. It is not easy to get recognized using sign language alone. Hence, recognition systems are required to improve their communication capabilities. The rapid growth in the field of Artificial Intelligence motivated us to build a gesture recognition system based on machine learning and/or deep learning techniques for improved performance. In this chapter, an image-based recognition system for American Sign Language (ASL) is designed using 1. Handcrafted features classified by Machine Learning algorithms, 2. classification using a pre-trained model through transfer learning and 3. classification of deep features extracted from a particular layer by machine learning classifiers. Among these three approaches, deep features extracted from DenseNet and classification using K-Nearest Neighbor (K-NN) yield the highest accuracy of about 99.2%. To make this system handy, low cost, and available to needy people, the Resnet 50 model is deployed in a Raspberry Pi 3b + microcontroller.

Mental health care has unique challenges and needs, unlike other medical fields. Complex biopsychosocial causation of psychiatric disorders demands advanced computational models for scientific probing. Artificial intelligence and machine learning (AI/ML) are showing promising leads in improvising psychiatry nosology, which in the current state lacks biological validity. Increasing mental health care needs can be addressed only with the appropriate use of advancing technologies. Increased accessibility to personal digital devices demonstrates the scope for sensitive behavioral evaluation amidst gathering large amounts of data. Patterns in, thus acquired, digital phenotypes can be effectively evaluated only through big data analysis techniques. This has the potential to open newer avenues of preventive as well as therapeutic psychiatry. Unique legal and ethical conundrums in clinical and research domains of psychiatry arise while managing one of the most vulnerable populations with health care needs, who may often approach facilities in a state of illness, unawareness, and diminished decision-making capacity. Secure blockchain technology amalgamating with AI/ML can enhance the applicability in such conditions in improving compliance, individualizing treatment, and enhancing research without compromising ethical standards. AI/ML is hoped to guide Interventional psychiatry, an evolving promising field that relies on neuroscientific approaches using multimodal data and neuromodulation techniques. The current chapter reviews the contributions of AI/ML and blockchain in various mental healthcare system domains; and proposes its potential in many other uncharted territories in this field.

Due to partial medical facilities accessible in some developing nations such as India, early disease prediction is challenging. Pneumonia is a deadly and widespread respiratory infection affecting the distal airways and alveoli. Pneumonia is responsible for high mortality rates and short- and long-term mortality in persons of all age groups. The spread of Pneumonia mainly depends on the immune response system of human beings. The symptoms of Pneumonia vary from person to person and also on the severity of this disease. In the 21st century, Artificial Intelligence (AI) is recommended as one of the early-stage disease diagnosis methods. This chapter discusses the uses of one of the AI subdomains, which Machine learning challenges and issues that researchers face while diagnosing early-stage pneumonia disease.

COVID-19, or Corona Virus Disease, has developed as a global epidemic, affecting nearly every country. Infected humans are multiplying at an exponential rate throughout the planet. With such many patients, healthcare facilities are in high demand. Although every government is putting up considerable effort to combat the epidemic, a lack of medical facilities, particularly in highly populated countries such as India, poses a significant issue. The fear of a pandemic has trapped everyone in residences, wreaking havoc on various industries. Pandemics are wreaking havoc on the hotel and tourist industries. Smart healthcare tourism is the newest IoT-based healthcare tourism application to gain traction. This paper outlines an Internet of Things-based health monitoring system that may be helpful for foreign visitors and hotel management throughout maintaining the health of both its guests and staff. The system will identify and examine the body’s many vital signs before telling the operator the condition of each person’s health. The study focuses on the application of IoT technology, which includes wearable sensors, to monitor health eminence, identify sickness, and provide online well-being facilities for the health tourism industry. 

Social media provides a wealth of user-generated data, including ratings and comments on various causes, products, diseases, and public policies. A new field of text mining called sentiment analysis uses a variety of techniques to filter out people's moods and emotions. The World Health Organization (WHO) has declared COVID-19 a pandemic, and people worldwide are fighting for their lives. As a result, people experience various physical and mental problems such as fear, anxiety, irritability, and unhappiness. This study uses sentiment analysis to examine how individuals feel about the COVID-19 epidemic affecting Indians. Tweets were collected from January 2020 to March 2020. Data have been extracted from Twitter using TweepyAPI, and Numpy, Pandas, and Matplotlib perform analysis based on subjectivity and polarity. Through an automated system, we analyzed the tweets and categorized them into three categories: positive, negative, and neutral. From our analysis, we discovered that initially, people started putting negative tweets, but over time, people's sentiments changed to positive and neutral comments. The results from the study concluded that initially, the situation was terrible and tragic, but with time, people were able to handle the situation. They got accustomed to a new lifestyle following measures to prevent infection from the COVID-19 virus.

Today's companies acknowledge the importance of Artificial Intelligence and IoT (- Internet of Things) to achieve quality and operational efficiency in supply chain performance. Numerous elements, such as shifting demands, routes, severe disruptions, and compliance problems, continuously impair supply chain systems. As a result, supply chains need to be monitored and continually optimized. And that's why we needed advanced technologies like Artificial Intelligence and IoT in the supply chain process. The vision of Industry 4.0 emphasizes global machine networks in an innovative factory environment capable of exchanging information and selfmonitoring. Supply chain resilience can be increased by utilizing AI and IoT technologies, often known as AIoT, which have recently been essential in enhancing supply chain performance. This study investigates the potential effects of Industry 4.0 and related technology advancements, such as Artificial Intelligence and IoT, on Supply Chain (SC) performance. Through an exploratory study, our research will assess the impact of AI and IoT on the efficiency of the industrial supply chain. This study aims to shed new light on the subject and offer suggestions for further research.