book

Digital Transformation in Healthcare 5.0

Name: Digital Transformation in Healthcare 5.0
ISBN: 9783111399119

by Rishabha Malviya, Sonali Sundram, Rajesh Kumar Dhanaraj, Seifedine Kadry

May 2024

Intermediate to advanced

378 pages

14h 50m

English

De Gruyter

Read now

Unlock full access

About the book
PrefaceForeword
List of contributors
J. Raj Kumar, G.S.N. Koteswara Rao, Jithendar Reddy Mandhadi, G.V. Radha, Roja Rani Budha, M.V. Kiran Kumar Chapter 1 The impact of blockchain technology on the healthcare system
1.1 Introduction1.2 The most important findings1.2.1 Blockchain1.2.2 What is the big deal about it?1.3 Blockchain-based healthcare applications1.3.1 Blockchain-based healthcare management applications1.3.2 Sharing global scientific data sharing1.3.3 Electronic health record1.3.4 Blockchain use cases in healthcare information management1.3.5 Management of enrolment data1.3.6 Security1.3.7 Supply chain management1.3.8 Clinical trials1.3.9 Medical devices interlinked to the web1.3.10 Healthcare IoMT devices1.3.11 Healthcare IoT construction and data security1.3.12 Artificial intelligence1.3.13 Blockchain makes use cases on IoMT1.3.14 Blockchain’s potential in the healthcare industry1.3.15 A single point of information1.3.16 Continuous data monitoring and accessibility around the clock1.3.17 Affordability1.3.18 A smart contract ensures that the rules are always the same1.4 Blockchain in healthcare: a practical application1.4.1 Controlling the supply network1.4.2 Make sure the drug’s ingredients are listed1.4.3 Medical records must be maintained in their entirety1.4.4 Treatment plan1.4.5 Claims are settled1.4.6 The company’s business model has changed1.4.7 Pharma firms1.5 What challenges does use blockchain in the healthcare sector present?1.5.1 Uncertainty1.5.2 Ownership of the data1.5.3 Cost1.5.4 Rules and regulations1.5.5 Capabilities for storage1.6 ConclusionAbbreviationsAuthors’ biography
Eswari naga Sai Priya Chintagunta, Manas Kumar Yogi Chapter 2 The role of metaverse in transforming healthcare: blockchain approach
2.1 Introduction2.2 Related work2.2.1 Contribution of metaverse to the healthcare2.2.2 Contribution of blockchain to the healthcare2.2.3 The contribution to healthcare by both the combination of metaverse and blockchain2.3 Proposed mechanism2.3.1 Decentralization of data through a secure network2.3.2 Creation of a virtual environment with high accessibility2.4 Experimental results2.4.1 Decentralization of data through a secure network2.4.2 Creation of a virtual environment with high accessibility2.4.3 The comparative performance between the popular methods and the healthcare systems2.5 Future scope2.6 ConclusionAbbreviationsAuthors’ biography
S.C. Vetrivel, T. Mohanasundaram Chapter 3 Blockchain-empowered metaverse healthcare systems and applications
3.1 Introduction3.1.1 Background and motivation3.1.2 Overview of block chain and metaverse technologies3.1.3 Significance of block chain in healthcare systems3.2 Existing metaverse applications in healthcare3.3 Challenges and limitations in current systems3.4 Blockchain technology in healthcare3.4.1 Explanation of blockchain fundamentals3.4.2 Key features and benefits of blockchain in healthcare3.5 Metaverse technology in healthcare3.5.1 Introduction to metaverse and virtual reality (VR)3.5.2 Potential advantages and limitations of metaverse in healthcare3.6 Integration of blockchain and metaverse in healthcare3.6.1 The synergy between blockchain and metaverse technologies3.6.2 Benefits and opportunities of integrating blockchain and metaverse in healthcare3.6.3 Technical considerations and challenges for integration3.7 Design and architecture of blockchain-empowered metaverse healthcare systems3.7.1 Proposed system architecture3.7.2 Role of blockchain in the architecture3.7.3 Data management and privacy considerations3.7.4 Security and trust mechanisms in the system3.8 Cases and applications3.8.1 Case studies and examples of blockchain-empowered metaverse healthcare systems3.9 Virtual patient monitoring and telemedicine3.10 Medical education and training in the metaverse3.11 Collaborative healthcare research and simulations3.11.1 Implementation and evaluation3.11.2 Details of the implementation process3.11.3 Performance evaluation and benchmarking3.11.4 User acceptance and feedback3.12 Challenges and future directions3.12.1 Key challenges and limitations in adopting blockchain and metaverse in healthcare3.12.2 Potential solutions and mitigation strategies3.12.3 Future research directions and emerging trends3.13 ConclusionAuthors’ biography
Deepak Kumar, Rishabha Malviya Chapter 4 Role of artificial intelligence in disease diagnosis
4.1 Introduction4.2 Technology used in AI-based disease detection4.2.1 Machine learning4.2.2 Convolutional neural network4.2.3 Natural language processing4.2.4 Quantum computing4.3 The role of AI in diagnosis of disease4.3.1 The advantages of using AI, including speed and accuracy4.4 Applications in disease diagnosis4.4.1 Alzheimer’s disease (AD) diagnosis4.4.2 Cancer diagnosis4.4.3 Cardiac disease4.4.4 Dermatological diagnosis4.4.5 Genetic diagnostic4.5 Applications in medical specialties4.5.1 Radiology4.5.2 Pathology4.5.3 Cardiology4.5.4 Ophthalmology4.5.5 Neurology4.6 AI challenges and limitations in disease diagnosis4.7 Future trend of AI in disease diagnosis4.8 ConclusionAuthors’ biography
Manivannan Karunakaran, Chandrasekar Venkatachalam, Mahesh T.R., Batri Krishnan, Nagaraj S. Chapter 5 Machine learning for twinning the human body
5.1 Introduction5.2 Data acquisition and preprocessing5.3 Image analysis and representation5.4 Sensor data integration5.5 Genetic and molecular profiling5.6 Machine learning models for digital twinning5.7 Applications and benefits5.8 Ethical and privacy considerations5.9 Challenges and future directions5.10 ConclusionAbbreviationsAuthors’ biography
M. Ashok, T. Pandiarajan, G. Kavitha, R. Saravanan, R. Kumar Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges
6.1 Introduction6.2 Data integration and interoperability6.2.1 Heterogeneous data sources6.2.2 Privacy and security6.2.3 Scalability and efficiency6.3 Data quality and reliability6.3.1 Data accuracy and completeness6.3.2 Data consistency and standardization6.3.3 Data relevance and context6.3.4 Data accuracy and timeliness6.3.5 Data bias and representation6.3.6 Data management and data protection6.3.7 Data verification and validation6.4 Privacy and security6.4.1 Protecting patient privacy6.4.2 Data breaches and data security risks6.4.3 Regulatory compliance6.4.4 Data sharing and interoperability6.4.5 Insider threats6.4.6 Data governance and accountability6.5 Scalability and performance6.5.1 Volume and speed of information6.5.2 Information integration and combination6.5.3 Computational complexity6.5.4 Real-time analytics6.5.5 Information capacity and recovery6.5.6 Information preprocessing and change6.5.7 Demonstrate preparing and optimization6.6 Predictive analytics and decision support6.6.1 Information openness and accessibility6.6.2 Data complexity and dimensionality6.6.3 Model change and endorsement6.6.4 Explainability and interpretability6.6.5 Clinical centrality and significant variables6.6.6 Real-time and lively estimates6.7 Ethical and legal considerations6.7.1 Privacy and confidentiality6.7.2 Informed consent6.7.3 Ownership and control of data6.7.4 Bias and discrimination6.7.5 Algorithmic transparency and interpretability6.7.6 Data security and cybersecurity6.7.7 Accountability and responsibility6.7.8 Long-term impact on the patient–doctor relationship6.8 Implementation and adoption6.8.1 Data integration and interoperability6.8.2 Scalability and infrastructure6.8.3 Stakeholder engagement and collaboration6.8.4 Management and organizational culture6.8.5 Education and skill development6.8.6 Cost-effectiveness and return on investment6.8.7 Regulatory and policy frameworks6.9 Interpretability and explainability6.9.1 Complex and black box models6.9.2 Trust and acceptance6.9.3 Contextual interpretation6.9.4 Individualized explanations6.9.5 Trade-off between interpretability and accuracy6.9.6 Longitudinal interpretation6.9.7 Uncertainty and reliability assessment6.9.8 Human-AI collaboration6.10 Data governance and standards6.10.1 Data quality and integrity6.10.2 Data integration and interoperability6.10.3 Data protection and security6.10.4 Consent and ethical considerations6.10.5 Data ownership and management6.10.6 Data sharing and collaboration6.10.7 Regulatory compliance6.11 Cost and resource management6.11.1 Infrastructure costs6.11.2 Data collection and integration costs6.11.3 Data storage and management costs6.11.4 Human resources and knowledge6.11.5 Scalability and performance optimization6.11.6 Cost-effectiveness evaluation6.11.7 Allocation and prioritization of resources6.11.8 Value-based care and reimbursement models6.12 ConclusionAuthors’ biography
Saurav Kumar Mishra, Jeba J. Praba, John J. Georrge Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare
7.1 Introduction7.2 Origin and trends of bioinformatics in healthcare7.2.1 Role of chemoinformatics7.2.2 Role of immunoinformatics7.2.3 Role of genomics7.2.4 Role of health informatics7.2.5 Role of artificial intelligence7.2.6 Additional information7.3 Origin and trends of big data analytics in healthcare system7.3.1 Primary features of big data analytics in healthcare system7.3.2 Type of big data analytics in healthcare7.4 ConclusionAbbreviationsAuthors’ biography
Upinder Kaur Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment
8.1 Introduction8.2 Digital twins and their role in health care8.3 Existing digital twin research in healthcare8.4 Machine learning techniques for digital twin8.5 Integration of healthcare systems and digital twin8.6 Case study: development of an immune digital twin for personalized healthcare8.7 Digital twins in medicine: open issues and research challenges8.7.1 Open issues8.7.2 Research challenges8.8 Conclusion and future directionsAuthors’ biography

Iram Fatima, Mehtab Alam, Ihtiram Raza Khan Chapter 9 Nanorobots in healthcare
9.1 Introduction9.1.1 Desirable traits of nanorobots9.2 Nanobots9.2.1 History of nanobots9.2.2 Types of nanobots9.2.3 Components of nanobots9.2.4 How does nanorobotics work?9.3 Nanotechnology in different sectors9.3.1 Nanotechnology in electronics and computing9.3.2 Energy and environment9.3.3 Materials science9.3.4 Medicine and healthcare9.3.5 Agriculture and food industry9.3.6 Textiles and apparel9.3.7 Automotive and transportation9.3.8 Construction and infrastructure9.4 Application of nanobots in healthcare9.4.1 Diagnostics9.4.2 Quantum dots9.4.3 Microarray9.4.4 Nanotubes9.4.5 Therapeutic intervention9.4.6 Nanocarrier-mediated drug delivery9.4.7 Nano-sized gene carriers9.4.8 Nanorobots for dental therapy9.4.9 Nanorobots in cardiac therapy9.4.10 Nanomedicine9.4.11 Breaking up kidney stones9.5 Challenges9.5.1 Technical challenges9.5.2 Biomedical challenges9.5.3 Ethical and societal challenges9.5.4 Safety and environmental challenges9.5.5 Research and collaboration challenges9.6 Are nanorobots dangerous?9.6.1 The promise of nanorobotics9.6.2 Challenges and concerns9.6.3 Environmental impact9.6.4 Regulation and oversight9.6.5 Mitigating risks9.7 ConclusionAuthors’ biography
Chirag Vashisht, Rohit Kaushik, Eva Kaushik Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy
10.1 Introduction10.1.1 Internet of things10.1.2 Medical cyber-physical systems: revolutionizing healthcare delivery and patient care10.1.3 Wireless body area network (WBAN)10.1.4 What is the background of WBAN?10.2 Literature review10.2.1 Aljabri et al. [14]10.2.2 Hussein et al. [15]10.2.3 Cui et al. [16]10.2.4 Yamanobe et al. [17]10.2.5 Carmona et al. [18]10.3 Methodology10.3.1 Authentication10.3.2 Location recognition10.3.3 Device authentication10.4 Challenges10.4.1 Security and privacy10.4.2 Interoperability and standardization10.4.3 Reliability and safety10.4.4 Training and education10.4.5 Ethical and legal considerations10.4.6 Cost and resource constraints10.4.7 Security of cloud infrastructure10.5 Case study10.5.1 Infusion pump10.6 Future scope10.7 ConclusionAbbreviationsAuthors’ biography
Pragya Mishra, J. Jayashree, J. Vijayashree, Prith Sharma Chapter 11 Integration of cognitive computing and AI for smart healthcare
11.1 Introduction11.1.1 Cognitive computing11.1.2 The power of cognitive computing11.2 Use cases of cognitive computing in smart healthcare system11.2.1 Heart disease detection11.2.2 Monitoring post-surgery ankle activity11.2.3 Pathology detection and monitoring11.2.4 Early diagnosis of cognitive decline11.2.5 Healthcare service robots11.2.6 Veterinary healthcare11.3 Descriptions of various cognitive computing methods11.3.1 Heart disease detection11.3.2 Monitoring post-surgery ankle activity11.3.3 Pathology detection and monitoring11.3.4 Early diagnosis of cognitive decline11.3.5 Healthcare service robots11.3.6 Veterinary healthcare11.4 ConclusionAbbreviationsAuthors’ biography
Anupama Angadi, Muralidhara Rao Patruni, Satya Keerthi Gorripati, Saraswathi Pedada Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope
12.1 Introduction12.2 Related work12.2.1 Limitations12.2.2 Proposals of a new similarity metrics12.2.3 Accuracy12.3 Background12.4 Categories of RS12.4.1 Collaborative RS12.4.2 Content RS12.5 Algorithms12.5.1 Nearest neighbors12.5.2 Matrix factorization methods12.5.3 Clustering-based RS12.6 Similarity metrics12.6.1 User-based collaborative RS12.6.2 Item-based collaborative system12.6.3 Content-based filters12.7 Evaluation metrics12.7.1 Precision and recall12.7.2 MAE12.7.3 RMSE12.8 Conclusion and future workAuthors’ biography
Shasank Kamineni, Meghana Tummala, Sai Yasheswini Kandimalla, Tejodbhav Koduru, V. M. Manikandan Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector
13.1 Introduction13.2 Levels of natural language processing13.2.1 Phonology13.2.2 Morphology13.2.3 Lexical analysis13.2.4 Syntactic13.2.5 Semantic13.2.6 Natural language semantic analysis may be divided into two categories13.2.7 Meaning representation methodologies13.2.8 Discourse13.2.9 Pragmatic13.3 Applications of NLP13.3.1 Sentimental analysis13.3.2 Text classification13.3.3 Chatbots and virtual assistants13.4 Applications of NLP in healthcare sector13.4.1 Documentation13.4.2 Computer-assisted coding13.4.3 Clinical trial matching13.4.4 Prior authorization13.4.5 Chatbots13.4.6 Risk managing13.4.7 Review management13.4.8 Root cause analysis13.5 NLP-based approaches in healthcare sector-review13.6 Research challenges in the NLP domain13.7 ConclusionAuthors’ biography
Babita Gupta, Rishabha Malviya, Sonali Sundram Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning
14.1 Introduction14.2 Glaucoma and the pathophysiological mechanisms of glaucoma14.3 Current clinical practices for the diagnosis of glaucoma14.4 Diagnosing glaucoma using machine learning14.4.1 Principal component analysis (PCA)14.4.2 Support vector machine (SVM)14.4.3 K-nearest neighbors (KNN)14.4.4 Deep convolutional neural networks (DCNNs)14.5 Implantable intraocular pressure sensor14.6 Design specifications and parameters14.7 System for measuring intraocular pressure14.8 Challenges in the detection of glaucoma14.9 Challenges and limitations14.9.1 The challenges associated with frameworks and libraries14.10 Limitations of datasets14.11 ConclusionAuthor biographies
Jaya Singh, Ranjana Rajnish, Deepak Kumar Singh Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis
15.1 Introduction15.1.1 Parkinson’s disease15.1.2 Different machine learning algorithm15.2 Aim of the study15.3 Dataset15.4 Proposed methodology15.5 Results15.6 Conclusion15.7 Future scopeAuthors’ biography
Index

Overview

The book "Digital Transformation in Healthcare 5.0: Metaverse, Nanorobots, and Machine Learning" is a comprehensive discussion of disruptive technologies and their applications in healthcare. The book starts with an overview of blockchain technology's impact on the healthcare sector, emphasizing its potential to improve data security and interoperability. The book also discusses the Metaverse's role in healthcare transformation, utilizing a blockchain method to improve patient care and medical practices. The book also　focuses on the interrelationships of Blockchain-Enabled Metaverse Healthcare Systems and Applications, highlighting innovative strategies.　It also introduces an Intraocular Pressure Monitoring System for Glaucoma Patients, demonstrating the integration of IoT and Machine Learning for improved care. The book winds up with a Machine Learning Approach to Voice Analysis in Parkinson's disease Diagnosis, demonstrating the potential of voice analysis as a non-invasive diagnostic tool.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Methods and Applications of Statistics in Clinical Trials, Volume 1: Concepts, Principles, Trials, and Designs

Publisher Resources

ISBN: 9783111399119

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills