Artificial Intelligence and Cybersecurity in Healthcare E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Artificial Intelligence and Cybersecurity in Healthcare provides a crucial exploration of AI and cybersecurity within healthcare Cyber Physical Systems (CPS), offering insights into the complex technological landscape shaping modern patient care and data protection.
As technology advances, healthcare has transformed, particularly through the implementation of CPS that integrate the digital and physical worlds, enhancing system efficiency and effectiveness. This increased reliance on technology raises significant security concerns. The book addresses the integration of AI and cybersecurity in healthcare CPS, detailing technological advancements, applications, and the challenges they present.
AI applications in healthcare CPS include remote patient monitoring, AI chatbots for patient assistance, and biometric authentication for data security. AI not only improves patient care and clinical decision-making by analyzing extensive data and optimizing treatment plans, but also enhances CPS security by detecting and responding to cyber threats. Nonetheless, AI systems are susceptible to attacks, emphasizing the need for robust cybersecurity.
Significant issues include the privacy and security of sensitive healthcare data, potential identity theft, and medical fraud from data breaches, alongside ethical concerns such as algorithmic bias. As the healthcare industry becomes increasingly digital and data-driven, integrating AI and cybersecurity measures into CPS is essential. This requires collaboration among healthcare providers, tech vendors, regulatory bodies, and cybersecurity experts to develop best practices and standards.
This book aims to provide a comprehensive understanding of AI, cybersecurity, and healthcare CPS. It explores technologies like augmented reality, blockchain, and the Internet of Things, addressing associated challenges like cybersecurity threats and ethical dilemmas.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Digital Prescriptions for Improved Patient Care are Transforming Healthcare Through Voice-Based Technology
1.1 Introduction
1.2 Literature Review
1.3 Proposed System
1.4 Implementation and Results
1.5 Conclusion
References
2 Securing IoMT-Based Healthcare System: Issues, Challenges, and Solutions
2.1 Introduction
2.2 Related Work
2.3 SHS Architecture, Applications, and Challenges
2.4 Security Issues in SHS
2.5 Security Solutions/Techniques Proposed by Researchers
2.6 Future Research Directions
2.7 Conclusion
References
3 Fog Computing in Healthcare: Enhancing Security and Privacy in Distributed Systems
3.1 Introduction
3.2 Case Studies
3.3 Challenges
3.4 Methods to Enhance Security and Privacy in Distributed Systems
3.5 Future Directions of Fog Computing in Healthcare
3.6 Conclusion
References
4 Blockchain Technology for Securing Healthcare Data in Cyber-Physical Systems
4.1 What is Healthcare Data?
4.2 Need of Maintaining Healthcare Data
4.3 Risk Associated with Healthcare Data
4.4 Cyber-Physical Systems (CPS)
4.5 Healthcare Cyber-Physical Systems (HCPS)
4.6 Blockchain Technology
4.7 Blockchain Technology in Healthcare Data
4.8 Blockchain-Enabled Cyber-Physical Systems (CPS)
4.9 Conclusion
References
5 Augmented Reality and Virtual Reality in Healthcare: Advancements and Security Challenges
Introduction
Advancements
Security Challenges
What is Augmented Reality?
What is Virtual Reality?
Revent Developments in AR and VR
Conclusion
References
6 Next Generation Healthcare: Leveraging AI for Personalized Diagnosis, Treatment, and Monitoring
6.1 Introduction
6.2 Benefits of AI in Healthcare
6.3 Challenges of AI in Healthcare
6.4 Approaches to Addressing Challenges in AI in Healthcare
6.5 Case Studies and Applications of AI in Healthcare
6.6 Future Directions and Opportunities in AI for Healthcare
6.7 Conclusion
References
7 Exploring the Advantages and Security Aspects of Digital Twin Technology in Healthcare
7.1 Introduction
7.2 Benefits
7.3 Security Considerations
7.4 Contribution in this Domain to Healthcare
7.5 Medical Device Development
7.6 Digital Twin Technology in Healthcare in Future
7.7 Continuous UI Upgrades
7.8 Conclusion
References
8 An Extensive Study of AI and Cybersecurity in Healthcare
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 AI Cybersecurity’s Significance for Healthcare
8.5 Difficulties with AI Cybersecurity
8.6 Conclusion
References
9 Cloud Computing in Healthcare: Risks and Security Measures
Introduction
Current State of Healthcare Industry
Cloud Computing in Healthcare
Benefits of Adopting Cloud in Healthcare
Drivers for Cloud Adoption in Healthcare
Cloud Challenges in Healthcare
Cloud Computing–Based Healthcare Services
Current Market Dynamics
Impact of Cloud Computing in Indian Healthcare Firms
Conclusion
References
10 Explainable Artificial Intelligence in Healthcare: Transparency and Trustworthiness
10.1 Introduction
10.2 Working of XAI in Healthcare
10.3 Explorable Artificial Intelligence Techniques and Methods in Healthcare
10.4 Interpretable Deep Learning Models
10.5 Clinical Decision Support System
10.6 Explainable Clinical Natural Language Processing
10.7 User-Centered Design of XAI Systems
10.8 Regulatory and Legal Perspectives in XAI for Healthcare
10.9 Ethical Considerations in Explainable Artificial Intelligence (XAI) for Healthcare
10.10 Strategies for Promotion of Accountable Use of XAI in Healthcare
Conclusion
References
11 Fuzzy Expert System to Diagnose the Heart Disease Risk Level
11.1 Introduction
11.2 Work Related
11.3 Expert Methods for Medical Diagnosis
11.4 Parameter Input
11.5 System Flow
11.6 Simulation and Result
11.7 Conclusion
References
12 Search and Rescue–Based Sparse Auto-Encoder for Detecting Heart Disease in IoT Healthcare Environment
12.1 Introduction
12.2 Related Works
12.3 Proposed Model
12.4 Results and Discussion
12.5 Conclusion and Future Work
References
13 Growth Optimization–Based SBLRNN Model for Estimate Breast Cancer in IoT Healthcare Environment
13.1 Introduction
13.2 Related Works
13.3 Proposed Model
13.4 Results and Discussion
13.5 Conclusion
References
14 Lightweight Fuzzy Logical MQTT Security System to Secure the Low Configurated Medical Device System by Communicating the IoT
14.1 Introduction
14.2 Methodology of FLS
14.3 Problem Identification
14.4 Proposed Approach
14.5 Result with Discussion
14.6 Conclusion
References
15 IoT-Based Secured Biomedical Device to Remote Monitoring to the Patient
15.1 Introduction
15.2 Internet of Things
15.3 IoMT
15.4 Biostatistical Techniques for Maintaining Security Goals
15.5 Healthcare IT System Through Biometric BioMT Approach
15.6 Conclusion
References
16 Fuzzy Interface Drug Delivery Decision-Making Algorithm
16.1 Introduction
16.2 Description and Problems
16.3 Methods
16.4 Application of Analgesia
16.5 Result
16.6 Discussion
16.7 Conclusion
References
17 Implementation of Clinical Fuzzy-Based Decision Supportive System to Monitor Renal Function
17.1 Introduction
17.2 Work Related
17.3 Methods
17.4 Discussion and Results
17.5 Conclusion
References
18 Deep Learning–Based Medical Image Classification and Web Application Framework to Identify Alzheimer’s Disease
18.1 Introduction
18.2 Proposed Methodology
18.3 Experiment Setup
18.4 Result
18.5 Discussion of Result
18.6 Conclusion
References
19 Using Deep Learning to Classify and Diagnose Alzheimer’s Disease
19.1 Introduction
19.2 Biomarkers and Detection of Alzheimer’s Disease
19.3 Methods
19.4 Model Evaluation and Methods
19.5 Conclusion
References
20 Developing a Soft Computing Fuzzy Interface System for Peptic Ulcer Diagnosis
20.1 Introduction
20.2 Methodology
20.3 Results
20.4 Conclusion
References
21 Digital Twin Technology in Healthcare: Benefits and Security Considerations
Introduction
Conclusion
References
22 Combating Cyber Threats Including Wormhole Attacks in Healthcare Cyber-Physical Systems: Advanced Prevention and Mitigation Techniques
22.1 Introduction to Cybersecurity in Healthcare Cyber-Physical Systems
22.2 Understanding Cyber Threats in Healthcare
22.3 Vulnerabilities in Healthcare Cyber-Physical Systems
22.4 Advanced Prevention Techniques
22.5 Mitigation Strategies for Cyber Threats
22.6 Emerging Technologies and Future Trends
22.7 Training and Awareness Programs
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Comparison table.
Table 1.2 Prescription report.
Chapter 2
Table 2.1 Scenarios related to privacy concerns in the context of SHS.
Table 2.2 Comparison with pre-existing survey related to the security of SHS.
Table 2.3 Types of SHS attacks.
Table 2.4 Access control-related security solutions.
Table 2.5 Privacy-related security solutions.
Table 2.6 Authentication-related security solutions.
Table 2.7 Trust management mechanisms for SHS.
Table 2.8 Intrusion Detection System (IDS) for SHS.
Chapter 11
Table 11.1 Fuzzy rate in input and output.
Table 11.2 Expert opinion test result.
Chapter 12
Table 12.1 Dataset report of 10 heart patients.
Table 12.2 Validation analysis of proposed model without AFO.
Table 12.3 Analysis of ISAE with AFO.
Chapter 13
Table 13.1 Analysis of proposed model for 70%-30%.
Table 13.2 Analysis of projected model for 80%-20%.
Chapter 14
Table 14.1 Characteristics of network traffic features.
Table 14.2 Performance survey of Secure-MQTT.
Chapter 15
Table 15.1 Various sorts of biometrics as solid and feeble.
Chapter 16
Table 16.1 Confusion of +ve and -ve records.
Chapter 18
Table 18.1 Parameteric details for AlexNet model and ResNet model.
Table 18.2 System specifications.
Table 18.3 Performance of Alzheimer’s disease detection system.
Table 18.4 Comparison table of AlexNet with other models.
Chapter 19
Table 19.1 300 subjects for demographic.
Table 19.2 Set size test and training.
Table 19.3 Parameters for VGG-19 model.
Chapter 20
Table 20.1 Three samples of the parameters from rat ulcer.
Table 20.2 The variable effort of FIS.
Table 20.3 Comparison of ROC curves.
List of Illustrations
Chapter 1
Figure 1.1 Schematic representation.
Figure 1.2 Graphical representation result.
Chapter 2
Figure 2.1 Three-tier architecture of SHS.
Chapter 3
Figure 3.1 Fog computing architecture in healthcare.
Figure 3.2 Remote patient monitoring in fog computing.
Figure 3.3 Clinic decision support in fog computing.
Figure 3.4 Smart Health 2.0 architecture in fog computing.
Chapter 4
Figure 4.1 Components of cyber-physical system (CPS).
Figure 4.2 Framework of cyber-physical system (CPS).
Figure 4.3 A block of blockchain.
Figure 4.4 Chain of blocks.
Figure 4.5 Chain of blocks with invalid reference.
Figure 4.6 Block structure.
Figure 4.7 Hashing and digital signature.
Chapter 5
Figure 5.1 AR and VR in healthcare.
Chapter 6
Figure 6.1 Examples of AI applications across the human lifespan [2].
Figure 6.2 Challenges of AI in healthcare.
Chapter 8
Figure 8.1 Medical process of patients [12].
Figure 8.2 Methodology for assessing cyber-susiliency.
Figure 8.3 AI cybersecurity in healthcare.
Chapter 10
Figure 10.1 Role of XAI in artificial intelligence.
Figure 10.2 Benefits of XAI in healthcare.
Figure 10.3 Challenges of XAI.
Figure 10.4 Working of XAI in healthcare.
Figure 10.5 Types of XAI techniques in healthcare.
Figure 10.6 Interpretable deep learning models in XAI.
Figure 10.7 Segments of explainable clinical NLP.
Figure 10.8 Ethical considerations in XAI in healthcare.
Figure 10.9 Strategies for accountable use of XAI in healthcare.
Chapter 11
Figure 11.1 Flow diagram of system.
Figure 11.2 Level of ECG.
Figure 11.3 Level of chest pain.
Figure 11.4 Level of sugar blood.
Figure 11.5 Level of cholesterol.
Figure 11.6 Level of blood pressure.
Figure 11.7 Patient age.
Figure 11.8 Heart rate of patients.
Chapter 12
Figure 12.1 Block diagram of heart disease risk prediction system.
Figure 12.2 Construction of ISAE model.
Figure 12.3 Graphical comparison of proposed model in terms of accuracy.
Figure 12.4 Precision analysis based on feature selection.
Figure 12.5 Comparative analysis of recall.
Figure 12.6 Specificity validation.
Figure 12.7 F1-score presentation.
Figure 12.8 AUC score.
Chapter 13
Figure 13.1 An overview of the proposed medical IoT-based architecture for bre...
Figure 13.2 An example of a bidirectional recurrent neural network’s recurrent...
Figure 13.3 Graphical analysis of proposed model.
Figure 13.4 Analysis of projected model on first dataset.
Figure 13.5 Validation analysis of projected perfect with existing procedures.
Figure 13.6 Comparative investigation of different DL models.
Figure 13.7 AUC score on two datasets.
Chapter 14
Figure 14.1 Message queuing telemetry transport publisher/subscriber/dealer me...
Figure 14.2 Developed denial-of-service detection system.
Figure 14.3 Suggested secure-MQTT structure.
Figure 14.4 Threat detection efficiency100 nodes.
Figure 14.5 Threat detection efficiency150 nodes.
Figure 14.6 Threat detection efficiency 200 nodes.
Figure 14.7 Threat detection efficiency 300 nodes.
Figure 14.8 Threat detection rate in case I.
Figure 14.9 Threat detection rate in case II.
Figure 14.10 Threat detection rate in case III.
Figure 14.11 Threat detection rate in case IV.
Figure 14.12 FPR in various time frames.
Figure 14.13 Communication rate.
Figure 14.14 Precision vs. time period graph.
Chapter 15
Figure 15.1 Devices and services of IoT.
Figure 15.2 IoMT of the medical industry.
Figure 15.3 BTU for verification.
Figure 15.4 IoT use by year.
Figure 15.5 Applications of IoT.
Figure 15.6 Ransomware attacks on different IoT as a percentage.
Figure 15.7 Biometric-based IoMT approach.
Chapter 16
Figure 16.1 The algorithm proposed in the scheme.
Figure 16.2 Structure of FIS.
Figure 16.3 FIS process in this research.
Figure 16.4 Data collection during the surgical interventions.
Figure 16.5 Data collection for patient undergoing for ANIi and ANI of time.
Figure 16.6 Values of ANI of increasing and decreasing.
Figure 16.7 ROC curves between sensitivity and 1 of FIS.
Chapter 17
Figure 17.1 The FIS of architecture.
Figure 17.2 The five layers of ANFIS.
Figure 17.3 FIS of methodology.
Figure 17.4 Fuzzy expert system of development.
Figure 17.5 Nephron function.
Figure 17.6 Function of blood sugar.
Figure 17.7 Function of BP.
Figure 17.8 Function of BMI.
Figure 17.9 Methodology of neuro system.
Figure 17.10 Graph of fuzzy except systems.
Figure 17.11 Parameters for adaptive neuro-FIS-systems.
Chapter 18
Figure 18.1 Parietal atrophy of an Alzheimer’s disease patient.
Figure 18.2 Distinct phases of dementia by MRI scans.
Figure 18.3 Image of the brain indicative of CN MCI and AD.
Figure 18.4 Samples of brain MRI scans.
Figure 18.5 Architecture of proposed methodology (scheme used to categorize AD...
Figure 18.6 Confusion matrices Alexnet.
Figure 18.7 Confusion matrices ResNet.
Figure 18.8 Accuracy value of AlexNet.
Figure 18.9 Loss value for AlexNet.
Figure 18.10 Curves for the receiver-operating characteristic of AlexNet.
Figure 18.11 Comparison of the AlexNet model.
Chapter 19
Figure 19.1 Process flow of the proposed framework.
Figure 19.2 Early detection of Alzheimer’s disease.
Figure 19.3 The architectural design of the 2D-M2IC model.
Figure 19.4 The planned model with other models.
Figure 19.5 Loss of validation accuracy.
Figure 19.6 Validation accuracy and loss loss.
Chapter 20
Figure 20.1 Fuzzy interference system.
Figure 20.2 Function of five petechia.
Figure 20.3 Functions of 1 mm ≤ ulcer length ≤ 2 mm.
Figure 20.4 Function of length ulcer ≥ 8 mm.
Figure 20.5 ROC curves for FCM.
Figure 20.6 ROC curves for ANFIS.
Chapter 21
Figure 21.1 Digital twin healthcare reference model [1].
Figure 21.2 Technology used by digital twin.
Chapter 22
Figure 22.1 Cybersecurity in healthcare physical system.
Figure 22.2 Cyber threats in healthcare systems.
Figure 22.3 Advanced prevention technique for cyber healthcare system.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Advances in Cyber Security
Series Editors: Rashmi Agrawal and D. Ganesh Gopal
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Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Artificial Intelligence and Cybersecurity in Healthcare
Edited by
Rashmi Agrawal
Pramod Singh Rathore
Ganesh Gopal Devarajan
and
Rajiva Ranjan Divivedi
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ISBN 9781394229796
Front cover images supplied by Adobe FireflyCover design by Russell Richardson
Preface
In the era of digital transformation, healthcare stands at the confluence of immense possibilities and complex challenges. The promise of better patient care, more accurate diagnostics, and personalized treatment is being realized daily. Yet, as with all revolutions, there are new challenges to face. Artificial Intelligence and Cybersecurity in Healthcare delves into the intricate dance between the vast potentials of artificial intelligence (AI) and the imperatives of cyber security in the healthcare industry.
The intersection of AI and cyber-physical systems in healthcare, from smart hospital rooms to wearable diagnostics, is reshaping the way we think about medical intervention and patient care. Such advancements are not just incremental; they have the potential to redefine the very paradigms of healthcare delivery. However, the introduction of these technologies also means that healthcare systems are more vulnerable to cyber threats, with potentially life-threatening consequences.
This book is a clarion call to researchers, practitioners, and enthusiasts alike. It outlines not only the myriad opportunities presented by AI in healthcare but also the urgent need for robust and proactive cybersecurity measures. Each chapter unravels a different dimension of this multidisciplinary field, drawing on real-world case studies, cutting-edge research, and expert opinions.
As you turn the pages, you’ll be invited to envision a future where AI-driven healthcare cyber-physical systems are both groundbreaking and secure. A future where technology augments human capabilities, rather than replacing or endangering them. This book serves as both a comprehensive guide and a challenge: to harness the power of AI for healthcare, while ensuring the utmost safety and security for patients.
In our pursuit of better health and well-being, it is essential to understand the balance of innovation and security. Artificial Intelligence and Cybersecurity in Healthcare is your roadmap to this brave new world.
Organization of the Book
This book is organized into twenty chapters. In Chapter 1, this Chapter discusses about the the study is based on machine learning and statistical models, were applied to develop a speech recognition system. As a result of the system, it can convert speech to text that can then be benefited for a variety of purposes, including voice commands, transcription services, and speech-to-text functions. Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs) are combined in the proposed system to enhance existing acoustic modelling techniques. Additionally, the report compares various existing approaches, identifies their flaws, and suggests ways to improve them. The proposed system is implemented and assessed using a publicly accessible dataset, and the findings are discussed.
In Chapter 2, Smart healthcare systems utilise wireless networks and information technology to facilitate the interchange and analysis of patient data. If the security and control measures of the smart healthcare system are insufficient, it becomes vulnerable to compromise by attackers. This vulnerability presents an opportunity for attackers to inflict harm upon patients, potentially leading to fatal consequences, all while remaining undetected. The intrinsic attributes of intelligent healthcare systems, such as their capacity for expansion, intricate nature, and diverse range of devices, pose significant challenges in promptly identifying and safeguarding against such cyber threats. This chapter endeavours to offer a methodical and all-encompassing examination of the security and privacy concerns linked to Smart Home Systems (SHS), as well as the security solutions put forth by the research community to safeguard SHS. Ultimately, the chapter culminates by presenting many prospective avenues for future research within the realm of safeguarding Internet of Medical Things (IoMT)-based intelligent healthcare systems.
In Chapter 3, this chapter explores the use of fog computing in healthcare along with enhancement of security and privacy in distributed systems. We provide an overview of the key concepts and architectures of fog computing and discuss the unique security and privacy challenges that arise in healthcare. We then review existing solutions and techniques for enhancing security and privacy in fog computing-based healthcare systems, including data encryption, access control, and privacy-preserving data analysis. Finally, we highlight some of the open research challenges and opportunities in this area, and provide recommendations for future research directions.
In Chapter 4, in this research chapter, users offers the capability of remote monitoring and management of physical systems, which may save time and money. But technology also brings along other difficulties, such interoperability, security, and privacy. Healthcare cyber-physical data has the ability to transform the healthcare sector, but for it to do so safely and effectively, it has to be carefully planned, managed, and secured. A significant quantity of data produced by healthcare cyber physical systems may be utilized to enhance patient care and guide healthcare policies. However, in order to safeguard this private information and keep patients’ confidence, healthcare organizations must likewise place a high priority on cyber-security.
In Chapter 5, the book chapter affords brief and general information regarding AR & VR technology over health domain, consisting of the blessings as well as capacity packages which additionally discuss regarding safety problems which were merged while taking the concept of AR & VR over health domain which brings the ability solutions to mitigate those challenges. Universal, advantages of AR & VR over health sector affords giant possibilities in developing patient effects, improving scientific training and study as well as allowing remote collaboration along telemedicine. But, addressing the safety demanding situations related to bringing those domains were crucial for making certain secure along best use over health domain meaningful, all models were extended by adding 3 layers at the end to improve their performance. The performance of the VGG19 model was found to be better and was able to classify almost all images belonging to 21 classes with an accuracy of 100% in training and 95.07% in testing data, followed by VGG16 with 93% and ResNet with 91% accuracy in testing data.
In Chapter 6, AI algorithms can analyze large volumes of patient data to create personalized treatment plans that consider individual medical history, genetics, and lifestyle factors. AI can also improve the accuracy and speed of diagnoses, as well as accelerate drug discovery. Remote monitoring and care can be facilitated by IoT devices, with AI analysis allowing for early detection of health issues. While AI holds tremendous potential for healthcare, data privacy, and security remain critical concerns, as does the need for transparency and accountability in the design and deployment of AI algorithms. This paper examines the benefits and challenges of AI in healthcare and demonstrates how it can improve patient outcomes and healthcare deliverys.
In Chapter 7, Computerized mechanism gives the true entity, such as structure, that helps us understand how well our systems are functioning and enables us to make better decisions regarding necessary improvements This ensures that there is always sufficient oxygen available for emergency transfusions. Nonetheless, there are several challenges to address before implementing digital twins in healthcare. Firstly, it is crucial to find a twin that closely matches the age, health, and other characteristics of the system being monitored. Additionally, the genetic profiles of the twins must be comparable to ensure the accuracy of the data. Lastly, both parties involved need to agree on the shared use of the twin’s information. Failure to address these considerations could lead to disregard of the dual usage of the twin technique by either party or clients.
In Chapter 8, we provide an analytical structure for examining these sociotechnical imaginaries, emphasising three key aspects: (a) healthcare and AI imaginaries; (b) their performativity; and (c) the socio-governmental background in which they are expressed. We determine three strategies for envisioning the foreseeable future of AI and medical treatments, namely strategies of 1) authorization, 2) advertisement, and 3) a sense of security. and Supported by an indispensable multimodal examination of the discourse of the regulatory initiative “Valuable AI,” these strategies add to the debate over policy regarding how to organise health care information in the age of artificial intelligence and how to encourage patients to make available their health information. Current methods of exchanging data limit the amount of personal information that may be shared. However, since healthcare AI systems depend on data to expand their capabilities, this kind of data deficiency makes it more difficult to create potential uses and reduces the amount of data required to support them. Three metrics in supply chains—resilience, long-term viability, and cyber-security—define how reliably they function without interruption.
In Chapter 9, Cloud computing is becoming increasingly popular in the healthcare sector, notably in the months following the COVID-19 outbreak. According to www.businesswire.com, the global computer industry in the healthcare sector will be worth $25.54 billion in 2024 and $89 billion in 2027 [1]. Cloud technology continues to be necessary in the healthcare sector in order to provide the greatest patient-centered experience. Infrastructure as a Service (IaaS), a cloud computing architecture, is the cloud service with the greatest growth at the moment, with a predicted 32% increase by 2027. The healthcare sector invests barely 10% of revenue on IT, compared to other industries that typically invest 25%. Since cloud computing was essential to the delivery of healthcare during the pandemic, every medical facility benefits from IT infrastructures in addition to the medical staff who provided care Steganography (HUGO), for creating stego images. This work provides a comparative analysis of one of the variants of CNN models specific for steganalysis and various pretrained models of computer vision applied to steganalysis.
In Chapter 10, in this chapter user give talks about the The opacity of traditional AI models, often referred to as “black boxes,” has raised concerns among clinicians, patients, and regulatory bodies [1]. The inability to decipher how AI systems arrive at their conclusions hampers trust and creates skepticism about their reliability and safety. This is particularly problematic in healthcare, where decisions can have life-altering consequences. To address these concerns, Explainable Artificial Intelligence (XAI) has emerged as a vital field of research. XAI aims to develop AI systems that not only provide accurate predictions but also offer clear and understandable explanations for their decisions [2]. By demystifying the decision-making process, XAI brings transparency to AI algorithms and empowers healthcare stakeholders to make informed decisions.
In Chapter 11, user explain the process of When diagnosing a patient, a feed-forward chain approach is created using a fuzzy rule-based inference engine. Data is collected from various places, including a hospital, where tests are taken to find their age, blood sugar level, gender, electrocardiogram and heart rate. The next phase involves the expert system for diagnostic tests in a hospital, where the created fuzzy value will serve as an input. The inference engine processes the crisp information, and then the expert system begins fuzzification and a comparison with the knowledge base. After the fuzzy sets’ value has been fuzzified, the defuzzification process starts with the expert system transforming it into a short value that is effective for reading. Ultimately, the patient’s heart problem is diagnosed based on a value calculated by the expert physician system employing fuzzy sets. Once the diagnosis is finalized, the expert system returns a result indicating whether the patient is at low, high, or dangerous risk for cardiovascular disease.
In Chapter 12, Forecasting cardiac disease is difficult since it takes both specialised of heart disease is a relatively new application of IoTs technology in healthcare systems. Although several studies have focused on diagnosing cardiac disease, the outcomes have been unreliable. In order to better assess cardiac illness, a suggested IoT system employs an enhanced Sparse Auto-Encoder (ISAE) model. Additionally, the classification accuracy is enhanced through the use of Artificial Fish Swarm Optimisation (AFO) to pick the features of the dataset. The smartwatch/heart monitor device worn by the patient keeps track of their BP and ECG readings. The ISAE is employed to categorise incoming sensor data as normal or abnormal, with SAE’s hyper-parameter tuning optimally set by SRO. The suggested ISAE is associated to algorithms to assess the scheme’s efficiency.
In Chapter 13, this chapter explains the The healthcare industry is the most prominent users of IoT technology. In the healthcare monitoring framework, IoT devices give data about individual patients. In addition, individuals can check their health using smart devices, so IoT is a crucial part of the healthcare administration system in general. Breast cancer, which is caused by the abnormal and rapid development of breast cells, is the most prevalent cancer in women. Early recognition of malignant cells is essential for reducing cancer-related mortality. Patients with breast cancer can benefit greatly from prompt diagnosis and treatment. To overcome the challenge of detecting breast cancer in its early stages, this study presents a medical Internet of Things (IoT) based diagnostic scheme based on a stacked bidirectional long short-term recurrent neural network (SBLRNN).
In Chapter 14, this chapter engages in discusses how unauthorized users may take command of network’s low-configuration devices by exploiting flaws in the MQTT protocol. This study introduces Secure-MQTT, a trivial fuzzy logical system based intrusion detection technique to identify harmful activities during IoT device connection. Using a fuzzy rule interpolation mechanism, the recommended approach employs a fuzzy logic-based system to identify the harmful behaviour of the node. Secure MQTT avoids a cumbersome rule site by using fuzzy rule interpolation, a rule generation method that operates on the fly. The suggested solution offers a reliable defense against DoS attacks for devices with limited resources. The results from the simulation demonstrate that the suggested strategy is superior to the current methods.
In Chapter 15, this book chapter presents an With IOMT, medical equipment may talk to one another without any human involvement. Security, privacy, connection, and interoperability are all obstacles to the broad use of IOMT. Numerous hacking attempts and security flaws have been discovered in IOMT-based systems. Login and password security mechanisms are incompatible with IoT-based technologies. Therefore, this chapter suggests an original and spoof-proof Biometric (BBIOMT) system. The BBIOMT method is an improved authentication system that does away with the problems that have plagued past attempts.
In Chapter 16, in this study, a brand-new method for solving this issue is introduced. The primary goal of this research is to define an automated system that can lead the medication supply decision-making process while also considering the effect of a specific clinical characteristic. This novel method will thus verify a particular physiological signal for use as a feedback variable in the titration of drugs. An FDS for the medication distribution method will be defined as a byproduct of the algorithm’s output in fuzzy rules and membership functions. The decision tree approach, the suggested method, derives its structure from a Fuzzy Inference System. Data group, preprocessing, FIS development, and result validation are the four steps established in this technique.
In Chapter 17, this chapter explains the purpose of this investigation is to use fuzzy and adaptive FIS to the problem of chronic kidney disease diagnosis. The primary goal of this innovation is to improve the precision of medical diagnostics used to identify health problems. Nephron function, glucose levels, systolic and diastolic blood pressure, age, weight, height, and smoking status are only a few of the aspects that must be considered while designing a neural fuzzy inference system. A patient’s stage of chronic renal illness, from 1 to 5, is defined by the output parameter based on several input criteria. The outcome will reflect the state of the patient’s kidneys right now. Therefore, specialists may benefit from using these methods when assessing the severity of chronic renal disease. The MATLAB environment is particularly well-suited to developing classical fuzzy and neural fuzzy inference systems.
In Chapter 18, there are now 18 real-time medical imaging apps that use deep learning techniques. In this research, use AlexNet 19 and Restnet50, two deep-learning network architectures, to categorize and identify cases of AD. Brain MRI pictures obtained from the Kaggle website were among the data used to 20 assess and test the suggested model in this research. A convolutional neural network (CNN) technique effectively identified AD. AlexNet and Restnet50 transfer 23 learning models were used to pre-train the CNNs. The experiments proved that the suggested technique outperformed the 24 existing methods regarding detection accuracy. Based on five assessment criteria (accuracy, F1 score, precision, sensitivity, and specificity), the AlexNet model obtained a remarkable 25 performance for the brain MRI datasets. AlexNet outperformed Restnet50 with an accuracy and specificity percentage of 95 and 97.21, similarly93.4 and 98 are the F1 Score and Sensitivity percentages. The suggested approach could enhance CAD 28 techniques for AD in clinical research.
In Chapter 19, this chapter explains the deep learning had great success in medical imaging. It is presently the standard approach for assessing medical images and has attracted a lot of attention to detect this disease. The deep model outperforms traditional machine learning methods for detecting AD. This article offers a summary of feature extraction and ad-related biomarkers approaches in addition to covering the use of deep learning techniques in AD diagnosis. It provides a comparison with different AD detection models. According to the findings, deep learning technology is useful for identifying.
In Chapter 20, this book chapter presents the primary objective of this research is to develop a unique, low-cost, dependable, and fast FES for peptic ulcer disease diagnosis. The Pasteur Institute provided a dataset of 100 male adult Wistar rats weighing between 200 and 250 g. This research suggests a computational method based on (FIS) for assessing peptic ulcers. Fuzzy C-Means is used to create the Fuzzy Inference System and the Adaptive Neuro-Fuzzy Inference System (ANFIS) model which is used to fine-tune it. The FIS’s performance measures using a ROC curve, with the FCM’s 90% correctness and the ANFIS’s 85% accuracy used as benchmarks. As a result, the Fuzzy Expert System may help to advance the field of precision medicine and treat peptic ulcer illnesses by increasing the accuracy and efficiency of medical operations. Hospitals and other healthcare systems might benefit from this adaptable Fuzzy system by increasing efficiency and productivity while decreasing care costs. It might be used in the medical field to minimize the potential for mistakes and physical labour.
In Chapter 21, the use of digital twins is a new development that offers personalized treatment, real-time monitoring, and preventative maintenance and has the potential to revolutionized healthcare. To safeguard private patient information and guarantee data correctness and integrity, the adoption of digital twin systems in healthcare also raises security issues that must be taken into account. The advantages of using digital twin technology in healthcare are covered in this chapter, along with security issues that healthcare providers must consider.
In Chapter 22, this chapter provides an in-depth exploration of cybersecurity in healthcare cyber-physical systems, with a special emphasis on understanding and combating a variety of cyber threats, including the sophisticated wormhole attacks. The chapter begins with a comprehensive introduction to cybersecurity in the healthcare sector, highlighting the critical role of cyber-physical systems and the inherent vulnerabilities they bring. It then delves into identifying common cyber threats faced by healthcare systems, including ransomware, data breaches, and insider threats, with a special focus on the mechanisms and impacts of wormhole attacks.
Dr. Rashmi Agrawal
Professor and Associate Dean
Manav Rachna International Institute of Research and Studies, Faridabad, India
Dr. Pramod Singh Rathore
Department of CCE, Manipal University Jaipur, India
Ganesh Gopal Devarajan
Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi - NCR Campus, Ghaziabad, Uttar Pradesh, India
Rajiva Ranjan Divivedi
Lecturer Computer Science (BPSC)U M S Bangalkhand, Kuchaikot, Gopalganj, India
1Digital Prescriptions for Improved Patient Care are Transforming Healthcare Through Voice-Based Technology
Preeti Narooka1* and Deepa Parasar2†
1Computer Science Engineering - AIML, Manipal University Jaipur, Rajasthan, India
2Computer Science Engineering - Data Science, Amity University Mumbai, Maharashtra, India
Abstract
In this paper, the study is based on machine learning and statistical models, which were applied to develop a speech recognition system. As a result of the system, it can convert speech to text that can then be used for a variety of purposes, including voice commands, transcription services, and speech-to-text functions. Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs) are combined in the proposed system to enhance existing acoustic modelling techniques. Additionally, the report compares and contrasts various existing approaches, identifies their flaws, and suggests ways to improve them. The proposed system is implemented and assessed using a publicly accessible dataset, and the findings are discussed.
Keywords: DNN, HMM, machine learning, healthcare, speech recognition, text, NLP
1.1 Introduction
How the physician consultancy process works is common knowlwdge. A person feels ill or unwell, they visit a doctor, the doctor writes the patient a prescription, the same is taken to the pharmacist, where the patient is provided with the medication as written by the doctor. This is exactly where a world of problems comes to light. The written prescription has had a history of unreadability, which has caused patients to misinterpret the prescription and take the medication at the wrong time or have received the wrong medication altogether from the pharmacist, who must have misinterpreted the prescription. Considering this issue, what used to come to our minds was what would happen if the doctor, instead of writing the prescription manually, would get us a typed and printed form of prescription. This idea was used by many software companies, which produced applications that would allow a nurse to type the prescription and gave the capability to either create a PDF of it or print a hard copy [2]. Such applications would also provide access to a search bar, which would search for the name of the medicine, and display the different variants of that medicine, for example, Nozolev, which is a medicine used against allergies. The application would display all the variants of Nozolev which are stored in the database. Such an application would also store prescriptions of different patients separately, and the former prescriptions of any patients can be accessed at any point of time by the doctor to revise any further medical decisions [6]. The facility of cloud storage is also sometimes provided to store large amounts of data, if this application is to be deployed in a large hospital. These applications were then further enhanced by using machine learning (ML), which allowed for faster creation of prescriptions [15]. For example, Nozolev is usually prescribed for 20 days, to be taken every alternate day, unless prescribed otherwise by the doctor. The application, through its experience, will learn this pattern, and then quickly fill up the dosage column with the learned data. Also, applications like these, using ML, learn the general medicines prescribed by the doctor for a particular diagnosis, which again, helps reduce the time of producing a prescription.
Even with all this working, such an application would still require a lot of will to be adapted to, instead of continuing with the conventional mode of prescribing, which is by writing, because it’s something all of us are most used to. So, considering the convenience factor in a prescription-creating application, something people are even more comfortable with than writing is speaking. Hence, an application which can recognize the spoken words by the doctor and place them under the correct column name, would be a much easier process than writing or typing, and would be much more powerful. Speech Recognition, Speech to Text, Text to Speech, has been in existence for a long time now, and many advancements can now be seen in voice assistants, like Google Assistant, Siri (Apple), Alexa (Amazon) and Cortana (Microsoft) [2, 4, 5]. The accuracy of recognition of the spoken word is appreciable and is expected to increase in the future. During the COVID pandemic, people were also seen making and taking notes using speech recognition. These note-making techniques would require the user to prompt the process of speech to text, and manually write the heading of every next topic. What if this Speech to Text can be used to produce prescriptions? The doctor must only utter the name of the medicine, the dosage, before or after meals, whether the medicine must be taken on an alternate day basis, etc., and digitally sign the prescription. This is where the aim of this project comes into the picture. It aims to combine the strengths of Speech to Text with prescription-producing software, to create something called the Voice Prescription [2, 6]. This application can listen to the doctor’s voice, as they prescribe the medicine, dosage, amount, and whether the medicine must be taken before or after meals and give some additional remarks. There will be the option to print a PDF of the prescription, or to share the PDF on WhatsApp. There’s also a feature that will save the recording of the doctor’s voice as he speaks out the prescription, to make available the same for the patient for future use.
1.2 Literature Review
1.2.1 Research Paper Survey
Abha Anand B. et al. (2021) [1] “Review Paper on HMM for Speech Recognition System” describes speech recognition and its applications, as well as the issues that occur and coping mechanisms, with more insights on HMM Model and its applications. Also, suitable cost-effective efficient replacements to the existing system may be found.
Mohammed Abrar Ahmed et al. (2020) [2] “Paper-less Prescription Using Voice” explains the purpose and implementation of a prescription mobile app for developing healthcare services. Using Android studio and Java an app has been developed which uses Google API to convert the prescription from audio to written format.
Neha Jain et al. (2019) [3] “Speech Recognition Systems - a Comprehensive Study of Concepts and Mechanism” explained the Mechanism, Challenges, and Algorithm (DTW and HMM) used in Speech Recognition. This paper briefs about traditional speech algorithms, along with comparison of these to existing speech recognition models and systems.
Sima Ajami et al. (2021) [4] “Use of speech-to-text technology for documentation by healthcare providers.” This paper directs the usage of speech recognition technology in the medical field and explores the issues associated with implementation and benefits attribute to it.
Vijay Prasad et al. (2015) [5] “Voice recognition system: speech-to-text.” This paper deals with voice recognition where the system is divided into two halves to ease implementation. One half is converting speech to text and the other half is to interpret the processed speech, by applying Hidden Markov Model (HMM) and Mel-frequency cepstral coefficients (MFCC).
Babu M, Hemchandhar et al. (2021) [6] “Voice Prescription with End-to-end Security Enhancements.” This paper shows a mobile-based speech recognition application using Natural language processing to interpret and deciding the essential words for generating prescription.
Ertopcu, Burak et al. (2017) [7] “A New Approach for Named Entity Recognition.” This paper gives insights into NLP, NER and methodology used such as Dummy, C45, KNN, NB, RF. This article explains various NLP libraries, such as TensorFlow, SpaCy, and Apache OpenNLP. Some of these libraries offer a pre-built, adaptable NER model. These libraries of models are compared based on training accuracy, F-score, prediction speed, model size, and training simplicity.
Qi Guo et al. (2020) [8] “Research on Named Entity Recognition for Information Extraction.” This paper discusses the Traditional NER method which uses Rule to Evolution with Deep Learning, NER Methods Named Entity Recognition combining transfer learning. Case studies are carried out on specific problem setups and implementations to determine the most successful approaches for currently used data mining techniques, and it was discovered that recurrent neural network provides effective modeling to construct various types of NER.
“Named Entity Recognition using Deep Learning” [9] elaborates on the Named entity Recognition using Deep learning where four famous techniques are used like Rule-based (Unsupervised Learning method, Features training method. Feature-based supervised training methods and Deep-Learning Techniques are discussed. The paper also explores the NER utilities and Applications with RNN and CNN Methods.
Prerana Das et al. (2018) [10]. In this essay, we discuss voice recognition, MFCC, HMM, vector quantization, and feature extraction. The system consists of two parts. The first part processes the acoustic input that is acquired by a microphone, and the second part interprets the processed signal before mapping it to words. The Hidden Markov Model (HMM) will be used to create a model for each letter. Using Mel Frequency Cepstral Coefficients (MFCC), features will be extracted. In addition to feature testing, vector quantization will be used to train and train features on the dataset. Voice recognition technology will underpin all aspects of home automation.
Ayushi Trivedi et al. (2020) [11]. This is a review of a text-to-speech and text-to-speech system. The speech recognition, speech to text, speech to text conversion, and machine learning technologies now in use are examined in this review study. Under the category of speech recognition, pre-processing, feature extraction (LPC, MFCC), acoustic model, language model, and pattern classification are further broken down and extensively described.
Alexandre Trilla (2009) [12] “Natural Language Processing Techniques in Text-To-Speech.” This paper depicts the usage of Natural Language Processing techniques in the production of voice from an input text. NLP for Speech Synthesis includes Text Normalisation, Tokenisation, sentence Segmentation, Part-of-Speech Tagging and word stress used in making a database.
Randhir Jagannath Patil et al. (2014) [13]. This research offered a framework for speech-based medicine prescriptions, which denotes the use of voice recognition technology to create a virtual prescription system. This system implements fundamental voice recognition theories such as pre-emphasis, feature extraction, and pattern comparison. Here, fuzzy decision logic is presented for selecting the suitable drug and the MFCC is employed as a feature extraction technique, the DTW as a pattern comparison. Thus, the suggested method will administer medication in response to a voiced ailment.
Rohit Pahwa et al. (2020) [14] “Speech Recognition System: A review.” The primary goal of voice recognition development is to make the technology simple to use for both technical and non-technical users. Additionally, the essay discusses the significance of Speech processing, which involves several different parameters, including pitch, duration, voice quality, intensity, signal-to-noise ratio, voice activity detection, and voice strength.
Tandel, Nishtha H., et al. (2020) [15]. This study, titled “Voice Recognition and Voice Comparison Using Machine Learning Techniques,” conducts a thorough literature review on both conventional and deep learning-based techniques for speaker identification and speech comparison. It also talks about publicly accessible datasets that academics use to compare voices and identify speakers.
1.2.2 Existing System Methodologies
One of the important tasks to make the system successfully meet the actual objective of developing it is to assess the existing systems and resolve the issues related to the system or its implementation. As for the current times, such systems are not being actively used in the medical practices in our country, although several proposals for developing such systems have been made. According to the research papers referenced so far, there are some mobile-based applications developed, based on speech recognition applications [2, 11]. Smartphones that have an application program interface made specifically for managing requests can access this application. For the purpose of creating prescriptions, natural language processing is utilized to read, understand, and identify key terms in text [7, 12]. In the current work, a voice-based mobile prescription application is discussed. These system designs are in a form of application where the page has empty textboxes for information such as name, gender, symptoms, diagnosis, etc. Each textbox has an individual voice recorder icon, to get voice input for each section. It allows a physician with a smartphone to generate prescriptions and view the patient’s former medical details. Access to the patient’s medical records in real-time improves the patient’s care, and in turn, health [4]. These models can avoid medication errors/medico legal issues/deaths due to prescription errors. Apart from mobile-based prescription systems, there are frameworks that signify the use of voice recognition techniques for medicine prescription, implementing basic theories for voice recognition such as pre-emphasis, feature extraction, and pattern comparison. MFCC is used as a feature extraction technique and DTW as a pattern comparison [3, 5, 10, 16]. Fuzzy decision logic is introduced here for appropriate medication [13, 17]. System performance is analyzed by creating symptom corpus for five people. Particularly assessing the capabilities of these systems to effectively produce the outcomes is most important. These applications are not very compatible with different types of systems, also not efficient in terms of storage of the data, and data transmission security. The system development complexity also becomes high in many proposed frameworks, due to which these systems are not being deployed for real-time uses.
1.2.3 Comparative Analysis
When comparing Google Speech Recognition API with other speech recognition APIs used in Python, several factors should be considered, including accuracy, language support, pricing, and ease of integration. Let’s explore a few popular alternatives and compare them to Google Speech Recognition.
1.2.3.1 Google Cloud Speech-to-Text API
Google Cloud Speech-to-Text is an API provided by Google Cloud that enables developers to convert spoken language into written text. It offers high accuracy and supports multiple languages. The API can handle various audio formats, including real-time streaming and asynchronous batch processing [2, 18]. It provides robust speech recognition capabilities, making it suitable for applications such as transcription services, voice assistants, and more. Integration is facilitated through client libraries and RESTful endpoints, ensuring ease of use for developers.
1.2.3.2 Microsoft Azure Speech Services
Microsoft Azure Speech Services is a suite of speech recognition APIs provided by Microsoft Azure. It offers accurate and customizable speech-to-text conversion, enabling developers to transcribe audio into written text. The API supports multiple languages and audio formats, providing flexibility for diverse applications [19]. It also includes features like speaker Diarization and language detection. Integration is simplified with the availability of a Python SDK and RESTful endpoints, making it easier for developers to incorporate speech recognition capabilities into their applications.
1.2.3.3 IBM Watson Speech to Text
IBM Watson Speech to Text is a service that converts spoken language into written text. It offers accurate speech recognition, even in noisy environments. The API supports multiple languages and can handle various audio formats [18]. It provides an easy-to-use Python SDK for seamless integration into applications. IBM Watson Speech to Text is a reliable solution for transcription services, voice command systems, and other speech-to-text applications.
1.2.3.4 CMU Sphinx
CMU Sphinx is an open-source speech recognition toolkit that allows developers to implement speech recognition functionality in their applications. It offers reasonable accuracy and supports multiple languages [17]. CMU Sphinx can handle different audio formats and provides Python libraries and APIs for easy integration. It is a free and open-source solution, making it accessible to developers with budget constraints.
The choice of API depends on your specific project requirements, budget, and desired level of accuracy. Google Cloud Speech-to-Text API is often considered a leading option due to its high accuracy and extensive language support, but the other APIs mentioned can also be suitable depending on your needs and constraints.
1.3 Proposed System
To address the challenges of the traditional prescription system, there is a need for a voice-based digital prescription system that automates and streamlines the prescription process, ensuring accuracy, efficiency, and improved patient safety. This system would allow healthcare professionals to dictate prescriptions using their voice, which would be transcribed and converted into digital prescriptions automatically.
The system that is to be proposed is a web-based application, which initially will require the doctor to perform the login authentication or register as a new user. After this, a new page will appear with a simple button that will allow the doctor to begin recording. Here is a proposed workflow explanation of how a voice-based prescription system could be implemented:
Speech Recognition: The system begins by capturing the voice input. This can be done using a microphone or any other voice input device. In programming, it is called audio source. The audio is stored in a variable created simultaneously. The audio is then converted into digital data using the Google Speech Recognition API [2]
