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Artificial Intelligence and Machine Learning in Neurology

Edited by Abhishek Kumar, Pramod Singh Rathore, Sachin Ahuja, and Manoj Manuja
Copyright: 2025   |   Expected Pub Date:2025//  |  Hardcover  |  
1578 pages
Price: $225 USD
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One Line Description
Unlock the future of brain health with this indispensable guide, which offers a comprehensive exploration of how artificial intelligence and machine learning are revolutionizing the diagnosis, treatment, and management of complex neurological disorders.

Audience
Academics, healthcare professionals, and policymakers on the cutting edge of technology and neurology

Description
As neurology grapples with some of the most challenging and pervasive health issues of our time, such as Alzheimers, Parkinsons, and stroke, AI offers the potential to transcend traditional barriers in treatment and management. Technologies such as machine learning models, neural networks, and cognitive computing are used to better understand and simulate brain functions, offering insights that are impossible for traditional analytical methods. Artificial Intelligence and Machine Learning in Neurology explores the pioneering intersection of neuroscience and artificial intelligence, offering a comprehensive examination of how machine learning and AI technologies are revolutionizing the fields of neurology and mental health. This book delves into cutting-edge research and practical applications of AI in diagnosing, treating, and managing neurological disorders. It discusses the development of intelligent diagnostic systems, personalized medicine approaches, and the potential of AI to analyze vast amounts of neurological data for insights. Additionally, the book addresses ethical considerations, challenges, and future prospects in the integration of AI into neurohealth sciences, making it an indispensable guide to this emerging technology.

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Author / Editor Details
Abhishek Kumar, PhD is an Assistant Professor and the Associate Director of the Computer Science and Engineering Department at Chandigarh University with more than 13 years of experience. He has authored seven books, edited more than 50 books, and published more than 170 publications in reputed national and international journals, books, and conferences. His areas of interest include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning.

Pramod Singh Rathore, PhD is an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with over 12 years of academic experience. He has published more than 85 papers in peer-reviewed national and international journals, books, and conferences, as well as numerous books. His research interests include computer networks, mining, and database management systems.

Sachin Ahuja, PhD is a Professor and Executive Director in the Department of Computer Science and Engineering at Chandigarh University. He has successfully led several funded projects in advanced areas like artificial intelligence, machine learning, and data mining, driving innovation and practical solutions. He has contributed to numerous high-quality academic books and served as a guest editor for special issues in reputed international journals.

Manoj Manuja, PhD is the Founder and CEO of Mystik Minds, a company dedicated to providing no-code AI education to students across diverse domains. Under his leadership, Mystik Minds has become a catalyst for empowering students from various backgrounds with essential AI skills, fostering inclusivity in technology education. He has hands-on expertise navigating the dynamic landscape of AI education, creating innovative and accessible learning pathways that resonate with learners from diverse fields.

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Table of Contents
Foreword
Preface
1. Ethical Frameworks for AI-Driven Healthcare: Genetic and Epidemiological Perspectives on Ethical AI Frameworks
Kailas D. Datkhile and Milind Pande
1.1 Introduction
1.1.1 The Role of AI in Healthcare
1.1.2 Importance of Ethical Frameworks
1.2 Ethical Considerations in AI-Driven Healthcare
1.2.1 Medical Ethics
1.2.2 Challenges in Applying Ethical Principles to AI
1.3 Genetic Perspectives on Ethical AI Frameworks
1.3.1 Personalized Medicine and AI
1.3.2 Data Protection of Genetics Information
1.3.3 Informed Consent and Genetic Testing
1.3.4 Equity in Access to Genetic Treatments
1.4 Ethical Frameworks for AI in Genetics
1.4.1 Principles of Autonomy, Beneficence, and Justice
1.4.2 Ensuring Patient Control Over Genetic Data
1.4.3 Utilizing AI-Derived Knowledge for the Patient’s Treatment
1.4.4 Fair Access to Available Services for Genetic Examinations and Interventions
1.5 Epidemiological Perspectives on Ethical AI Frameworks
1.5.1 AI and Disease Surveillance
1.5.2 Public Health Interventions and AI
1.5.3 Information Confidentiality Applying to Consent in Epidemiology Designs
1.5.4 Bias in Algorithmic Decision-Making
1.6 Ethical Frameworks for AI in Epidemiology
1.6.1 Principles of Transparency, Accountability, and Fairness
1.6.2 Responsible Use of AI-Powered Insights
1.6.3 Fair Division of AI Advantages Across Social Groups
1.7 Conclusion
References
2. Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment
Abhijeet R. Katkar and U. P. Waghe
2.1 Introduction
2.1.1 Summary of AI in Medicine
2.1.2 Importance of Ethical Considerations in AI Deployment
2.2 Ethical Principles in AI Deployment
2.2.1 Principles of Beneficence, Nonmaleficence, Autonomy, and Justice
2.2.2 Explainability and Transparency in AI Algorithms
2.2.3 The Importance of Accountability and Responsibility in AI Decision-Making
2.3 Challenges in AI Deployment in Urology and Gastroenterology
2.3.1 Data Privacy and Security Concerns
2.3.2 Bias and Fairness in AI Algorithms
2.3.3 Clinical Integration and Acceptance of AI Technologies
2.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology
2.4.1 Data Governance and Management
2.4.2 Safeguarding Patient Consent and Sensitive Information
2.4.3 Addressing Bias within AI Algorithms
2.4.4 Clinical Validation and Evaluation of AI Technologies
2.5 Case Studies
2.5.1 Application of AI Technology in Urology with Regard to Chronic Prostate Cancer
2.5.2 The Role of AI in Gastroenterology, with Relation to Diagnosing Other Digestive Tract Ailments
2.6 Future Directions and Recommendations
2.6.1 Progress of AI Ethics and Regulation
2.6.2 Collaboration Between Stakeholders for Ethical AI Deployment
2.6.3 Continuous Monitoring and Evaluation of AI Technologies
2.7 Conclusion
References
3. Bias Mitigation and Fairness in AI Healthcare Applications: Addressing Bias and Equity in AI-Driven Healthcare Solutions
Dhanaji Wagh and Prashant S. Jadhav
3.1 Introduction
3.1.1 AI in Healthcare
3.1.2 Bias Mitigation and Fairness
3.2 Bias in AI Healthcare Applications
3.2.1 Sources of Bias in AI Algorithms
3.2.2 Impact of Bias on Healthcare Equity
3.3 Strategies for Bias Mitigation in AI Healthcare
3.3.1 Diverse and Representative Training Data
3.3.2 Designing Algorithms in a Clear Manner
3.3.3 Auditing and Measuring for Fairness
3.4 Promoting Equity in AI Healthcare
3.4.1 Accessibility of AI Technologies
3.4.2 Addressing Unique Needs of Marginalized Communities
3.4.3 Designing for Inclusivity
3.5 Case Studies and Examples
3.5.1 Real-World Examples of Bias in Healthcare AI
3.5.2 Effective Approaches for Equity Promotion and Bias Mitigation
3.6 Future Directions and Challenges
3.6.1 Emerging Trends in Bias Mitigation
3.6.2 Ethical and Legal Considerations
3.7 Conclusion
References
4. Regulatory Compliance and Data Governance in AI-Driven Healthcare: Legal and Regulatory Considerations for AI-Driven Healthcare Solutions
Rahul S.S. and Satish V. Kakade
4.1 Introduction
4.1.1 An Overview of Healthcare Options Based on AI
4.1.1.1 Benefits of AI-Driven Healthcare Solutions
4.1.1.2 AI-Driven Solutions in Healthcare—Implementation Challenges
4.1.2 Importance of Compliance with Regulations and Governance of Data
4.2 Legal and Regulatory Frameworks
4.2.1 Health Insurance Portability and Accountability Act
4.2.2 General Data Protection Regulation
4.2.3 National Laws and Guidelines
4.3 Data Governance in AI-Driven Healthcare
4.3.1 Establishing Data Governance Frameworks
4.3.2 Ensuring Data Quality, Security, and Privacy
4.3.3 Ethical Considerations and Best Practices
4.4 Regulatory Compliance Challenges
4.4.1 Sensitive Patient Data
4.4.1.1 Challenges of Regulatory Compliance
4.4.1.2 Techniques for Resolving Compliance Issues—Regulations
4.4.2 Owning Data: The Legal Maze
4.4.3 Assigning Responsibility for AI Deficiencies and Defaults
4.5 Ethical Guidelines for AI in Healthcare
4.5.1 European Commission’s Ethical Guidelines for Trustworthy AI
4.5.2 Ethical Considerations in AI Algorithm Design and Deployment
4.6 Case Studies
4.6.1 Successful Implementation of Data Governance Frameworks
4.6.2 Challenges Faced and Lessons Learned
4.7 Future Trends and Considerations
4.7.1 Emerging Regulatory Trends in AI-Driven Healthcare
4.7.2 Possible Effects of New Technologies on Fulfilling Regulatory Obligations
4.8 Conclusion
References
5. Ensuring Responsible Data Use in Healthcare AI Applications: Radiological and Surgical Approaches to Responsible AI Data Usage
Asif Tamboli and Kalpana Malpe
5.1 Introduction
5.1.1 Overview of Healthcare AI Applications
5.1.2 Significance on Responsible Use of Data
5.2 Responsible Data Use in Radiological AI Applications
5.2.1 Role of AI in Radiological Imaging
5.2.2 Data Privacy and Anonymization
5.2.3 Consent Management for AI Data Usage
5.2.4 Strategies for Addressing Bias
5.2.5 Transparency and Monitoring in AI Algorithms
5.3 Responsible Data Use in Surgical AI Applications
5.3.1 Utilizing AI in the Preoperative Planning and Surgical Decision-Making Process
5.3.2 Data Security and Patient Privacy in Surgical AI
5.3.3 Data Security: Encryption and Protection Measures
5.3.4 Methods of Ensuring Secure Transmission of Data
5.3.5 Use of AI in Interpretable Algorithms for Surgery
5.4 Multidisciplinary Approaches to Responsible AI Data Usage
5.4.1 Collaboration Between Radiologists, Surgeons, and Data Scientists
5.4.2 Ethical Considerations in AI Development
5.4.3 Compliance with Regulatory Frameworks
5.5 Case Studies and Best Practices
5.5.1 Successful Implementations of Responsible Data Use in Healthcare AI
5.5.2 Recap of the Case Real-Life Use Studies
5.6 Radiological and Surgical Approaches to Responsible AI Data Usage
5.6.1 Radiological Approaches
5.6.2 Surgical Approaches
5.6.3 Collaboration and Compliance
5.7 Conclusion
References
6. Implementing Secure Health Data Exchange with Blockchain: Orthopedic and Ophthalmological Insights into Secure Health Data Exchange
Patil Nitin S. and Mahendra Alate
6.1 Introduction
6.1.1 Overview of Health Data Exchange
6.1.2 Importance of Security in Health Data Exchange
6.1.3 Role of Blockchain Technology in Secure Health Data Exchange
6.2 Orthopedic Insights into Secure Health Data Exchange
6.2.1 Orthopedic Data Exchange Obstacles
6.2.2 Implementing Blockchain in the Sharing of Orthopedic Data
6.2.3 Case Studies and Stories of Success
6.3 Ophthalmological Insights into Secure Health Data Exchange
6.3.1 Challenges in Sharing Ophthalmological Information
6.3.2 Using Blockchain Technology for Data Sharing in Ophthalmology
6.3.3 Case Studies and Success Stories
6.4 Blockchain Technology for Health Data Exchange
6.4.1 Understanding Blockchain Technology
6.4.2 Advantages and Disadvantages of Blockchain Technology with Respect to Heath Data Exchange
6.4.2.1 Advantages of Applying Blockchain Technology in Health Data Exchange
6.4.2.2 Limitations of Blockchain in Health Data Exchange
6.5 Regulatory and Legal Considerations
6.5.1 HIPAA Compliance and Health Data Security
6.5.2 GDPR and Protection of Sensitive Health Information
6.5.3 Legal Implications of Blockchain in Health Data Exchange
6.6 Future Trends and Challenges
6.6.1 New Developments in Technological Health Data Exchange
6.6.2 Challenges and Opportunities in Implementing Blockchain
6.6.3 Future Directions for Secure Health Data Exchange
6.7 Conclusion
References
7. Securing Clinical Trial Data with Decentralized Technologies and Exploring Blockchain Applications in Modern Healthcare Management
Patange Aparna P. and Kadam Shrikant Rangrao
7.1 Introduction
7.2 Related Work
7.3 Overview of Blockchain Technology
7.4 Methodology
7.5 Blockchain Applications in Clinical Trial Data Management
7.6 Decentralized Technologies in Healthcare Management
7.7 Results and Discussion
7.8 Conclusion
References
8. Blockchain-Enabled Healthcare Ecosystems: Scalability, Security, and Interoperability
Mario Antony and Trupti S. Bhosale
8.1 Introduction
8.1.1 Overview of Blockchain Technology
8.1.2 Importance of Blockchain in Healthcare
8.2 Scalability Challenges in Healthcare Blockchain
8.2.1 Scalability Issues in Traditional Blockchain Networks
8.2.2 Impact of Scalability on Healthcare Data Management
8.2.3 Solutions for Improving Scalability
8.3 Security Considerations in Blockchain-Enabled Healthcare
8.3.1 Security Policies in Healthcare Data Management
8.3.2 Inherent Security Features of Blockchain
8.3.3 Challenges in Ensuring Security
8.4 Interoperability in Blockchain-Enabled Healthcare Systems
8.4.1 Importance of Interoperability in Healthcare Data Exchange
8.4.2 Role of Blockchain in Promoting Interoperability
8.4.3 Integration Standards and Guidelines for Conformity
8.4.3.1 HL7’s Fast Healthcare Interoperability Resources
8.4.3.2 Integrating the Healthcare Enterprise
8.5 Case Studies and Applications
8.5.1 Blockchain in Healthcare
8.5.2 Practical Uses of Blockchain Technology in Healthcare
8.5.3 Success Stories and Lessons Learned
8.6 Future Trends and Research Directions
8.6.1 Blockchain Technology: New Trends
8.6.2 Research Opportunities to Address Issues in Scalability, Security, and Interoperability
8.6.3 The Possibilities of Blockchain Applications in Healthcare
8.7 Recommendations for Implementing Blockchain in Healthcare
8.7.1 Implications for the Future of Healthcare
8.8 Conclusion
References
9. Advanced Threat Detection in Health Information Systems with Cybersecurity Technologies in Modern Healthcare Applications
Shantanu Kulkarni and Shinde Patil Girisha Suresh
9.1 Introduction
9.2 Overview of Health Information Systems (HIS)
9.3 Methodology
9.4 Cybersecurity Threats in Healthcare
9.5 Advanced Threat Detection Technologies
9.6 Result and Discussion
9.7 Conclusion
References
10. Interoperability and Standardization in Healthcare System Integration: Interfacing Wearable Devices with Electronic Health Records
Nikhilchandra Mahajan and Kalpana Malpe
10.1 Introduction
10.1.1 Wearable Technology in Healthcare
10.1.2 Importance of Interoperability and Standardization
10.2 Challenges in Integrating Wearable Devices with EHRs
10.2.1 Data Privacy and Security Concerns
10.2.2 Data Quality and Consistency Issues
10.2.3 Device Connectivity Challenges
10.2.4 Challenges on Interoperability
10.3 Standards and Frameworks for Interoperability in Healthcare System Integration
10.3.1 Preexisting Frameworks and Guidelines
10.3.2 HL7 FHIR—A Standard for Healthcare Data Exchange
10.4 Interfacing Wearable Devices with Electronic Health Records
10.4.1 Standardized Data Models for Wearable Device Data
10.4.2 Communication Protocols and Device Interfaces
10.4.3 Ensuring Compatibility and Interoperability
10.5 Collaboration Among Stakeholders for Interoperability and Standardization
10.5.1 Role of Healthcare Providers
10.5.2 Participation of Device Manufacturing Companies
10.5.3 Contribution of Standards Development Organizations
10.5.4 Regulatory Considerations
10.6 Case Studies and Best Practices
10.6.1 Successful Examples of Wearable Device Integration
10.6.2 Best Practices for Ensuring Interoperability and Standardization
10.7 Future Directions and Recommendations
10.7.1 Fusing Healthcare with Technology
10.7.2 Recommendations for Systems and Stakeholders Involved in Providing Healthcare Services
10.8 Conclusion
References
11. Fundamentals of Predictive Analytics in Healthcare: Nephrological and Pulmonological Fundamentals of Predictive Analytics
Patil Dilip P. and Rasika Chafle
11.1 Introduction to Predictive Analytics in Healthcare
11.1.1 Overview of Predictive Analytics
11.1.2 Importance of Healthcare Predictive Analytic Powers
11.2 Fundamentals of Nephrological Predictive Analytics
11.2.1 Nephrological Diseases and Their Implications
11.2.2 Data Sources for Nephrological Predictive Analytics
11.2.3 Major Techniques for Predictive Analytics in Nephrological Disorders
11.2.4 Illustrations in Predicting Nephrology Analytics
11.3 Applications of Predictive Analytics in Nephrology
11.3.1 Early Detection of CKD
11.3.2 Predictive Modeling for Renal Replacement Therapy
11.3.3 Personalized Treatment Plans for Nephrological Patients
11.3.4 Outcome Prediction in Nephrology
11.4 Challenges and Opportunities in Nephrological Predictive Analytics
11.4.1 Quality and Accessibility of Data
11.4.2 Integration with Existing Healthcare Systems
11.4.3 Ethical Considerations
11.4.4 Anticipated Developments in Predictive Analytics of Nephrology
11.5 Fundamentals of Pulmonological Predictive Analytics
11.5.1 Pulmonological Diseases and Their Impact
11.5.2 Data Sources for Pulmonological Predictive Analytics
11.5.3 Essential Techniques for Predictive Analytics in Pulmonological Diseases
11.5.4 Case Studies in Pulmonological Predictive Analytics
11.6 Applications of Predictive Analytics in Pulmonology
11.6.1 Early Detection of COPD
11.6.2 Predictive Modeling for an Asthma Attack
11.6.3 Automated Plans of Care for Pulmonological Patients
11.6.4 Outcome Prediction in Pulmonology
11.7 Challenges and Opportunities in Pulmonological Predictive Analytics
11.7.1 Data Interoperability and Standardization
11.7.2 Patient Privacy and Data Security
11.7.3 Integration with Clinical Workflows
11.7.4 Future Directions in the Analytics of Predictive Lung Health Data
11.8 Conclusion
References
12. Advanced Techniques in Predictive Analytics: Ensemble Methods and Feature Engineering for Healthcare Predictions
Nelson Nishant Kumar Lyngdoh and Dheeraj Mane
12.1 Introduction
12.1.1 Predictive Analytics in Healthcare
12.1.2 Importance of Ensemble Methods and Feature Engineering
12.2 Ensemble Methods in Healthcare Predictions
12.2.1 Introduction to Ensemble Learning
12.2.2 Different Types of Ensemble Methods
12.2.3 Benefits and Challenges of Ensemble Methods in Healthcare
12.2.3.1 Advantages of Healthcare Ensemble Techniques
12.2.3.2 Problems of Ensemble Methods in Healthcare
12.2.4 Case Studies and Applications in Healthcare
12.3 Feature Engineering in Healthcare Predictions
12.3.1 Importance of Feature Engineering in Predictive Modeling
12.3.2 Common Feature Engineering Techniques
12.3.3 Feature Selection Strategies in Healthcare Data
12.4 Ensemble Methods and Feature Engineering
12.4.1 Implementing Ensemble Methods in Healthcare Predictive Modeling
12.4.2 Best Practices for Feature Engineering in Healthcare Data
12.4.3 Libraries and Tools for Group Learning and Feature Engineering
12.5 Challenges and Limitations
12.5.1 Interpretability Versus Performance Trade-Off
12.5.2 Computational Complexity and Scalability Issues
12.5.3 Data Quality and Ethics with Feature Engineering
12.6 Future Directions and Emerging Trends
12.6.1 Advances in Ensemble Methods and Feature Engineering
12.6.1.1 Progress in Ensemble Approaches
12.6.1.2 Progress Made in Feature Engineering
12.6.2 Integration with Other Advanced Analytics Techniques
12.6.2.1 Deep Learning
12.6.2.2 Reinforcement Learning
12.6.3 Potential Impact on Healthcare Delivery and Patient Outcomes
12.7 Conclusion
References
13. Leveraging Machine Learning for Predictive Healthcare Models: Hematological and Rheumatologic Approaches to Machine Learning in Healthcare
Hemchandra V. Nerlekar and Prashant S. Jadhav
13.1 Introduction
13.1.1 Overview of ML in Healthcare
13.1.2 Importance of Predictive Models in Hematological and Rheumatologic Conditions
13.2 Background
13.2.1 Summary on Blood and Joint Health Conditions
13.2.2 Current Challenges in Diagnosis and Treatment
13.3 Machine Learning Techniques
13.3.1 Overview of ML Algorithms Used in Healthcare
13.3.2 Specific Algorithms Suitable for Hematological and Rheumatologic Conditions
13.4 Data Collection and Preprocessing
13.4.1 Sources of Data for Hematological and Rheumatologic Conditions
13.4.2 Preprocessing Steps to Prepare and Make the Data ML Ready
13.5 Feature Selection and Engineering
13.5.1 Importance of Feature Selection in Healthcare Data
13.5.2 Methods of Feature Engineering Using Hematological and Rheumatologic Data
13.6 Model Development
13.6.1 Creating Predictive Models for Hematological and Rheumatologic Disorders
13.6.2 Training and Validation Strategies
13.7 Clinical Applications
13.7.1 Practical Uses of ML Technology in Hematological and Rheumatologic Medicine
13.7.2 Impact on Diagnosis, Treatment, and Patient Outcomes
13.8 Challenges and Future Directions
13.8.1 Current Concerns Associated with Implementing ML in Healthcare
13.8.2 Aspects for the Recombinant Research Activities in the Few Prospective Years
13.9 Conclusion
References
14. AI-Driven Risk Assessment Models for Proactive Glaucoma Monitoring
Abhay D. Havle and Kalpana Malpe
14.1 Introduction
14.2 Related Work
14.3 Proposed Methodology
14.4 Result and Discussion
14.5 Conclusion
References
15. Next-Generation AI/ML Algorithms for Health Monitoring: Deep Learning and Neural Network Architectures
Satish V. Kakade and Shyamala Moantri
15.1 Introduction
15.1.1 Overview of Health Monitoring
15.1.2 Importance of AI/ML in Health Monitoring
15.2 Traditional Methods Versus AI/ML in Health Monitoring
15.2.1 Limitations of Traditional Health Monitoring Methods
15.2.2 Advantages of AI/ML in Health Monitoring
15.2.3 Case Studies
15.2.3.1 Case Study: Monitoring Devices That Can Be Worn All the Time
15.2.3.2 Case Study: Using AI in the Analysis of Medical Images
15.3 DL Architectures for Health Monitoring
15.3.1 Introduction to DL
15.3.2 CNNs for Health Monitoring Based on Images
15.3.3 RNNs for Time-Series Health Data
15.3.4 LSTM Networks for Sequential Data Analysis
15.3.5 Case Studies
15.3.5.1 Case Study: Locating DR with CNNs
15.3.5.2 Case Study: Predicting Heart Failure Using LSTMs
15.4 Neural Network Architectures for Health Monitoring
15.4.1 Introduction to NN
15.4.1.1 Various Types of NNs
15.4.2 Multilayer Perceptron Networks for Health Data Analysis
15.4.3 Radial Basis Function Networks for Classification Tasks
15.4.4 Self-Organizing Maps Used for Clustering Health Data
15.4.5 Case Studies
15.4.5.1 Evaluating the Risk of Getting Heart Disease Using DNNs
15.4.5.2 Using CNNs for Skin Cancer Detection
15.5 Applications of DL and NN Architectures in Health Monitoring
15.5.1 Remote Patient Monitoring
15.5.2 Diagnosing a Disease
15.5.3 Tailor-Made Medicine
15.5.4 Wearable Devices and Sensors
15.5.5 Case Studies
15.5.5.1 Applying DNNs in Assessment of CVD Risk
15.5.5.2 Deployment of CNNs for Skin Cancer Detection
15.6 Challenges and Future Directions
15.6.1 Data Privacy and Security
15.6.2 Ethical Considerations
15.6.3 Explainable AI or ML and Their Analysis
15.6.4 Integration with Other Technologies
15.6.5 Future Research Directions
15.7 Conclusion
References
16. AI-Driven Personalized Care for Chronic Disease Patients Tailoring Treatments and Interventions Using AI for Conditions Such as Diabetes, Hypertension, and Heart Disease
S. T. Thorat and Fazil Sheikh
16.1 Introduction
16.2 Literature Review
16.3 AI Technologies in Chronic Disease Management
16.4 Case Studies
16.5 Tailoring Treatments and Interventions
16.6 Ethical and Regulatory Considerations
16.7 Future Directions
16.8 Conclusion
References
17. AI in Diabetes Management: Personalized Insulin Dosing and Glucose Monitoring Innovations in Diabetes Care Through AI for Continuous Glucose Monitoring and Insulin Therapy Optimization
Gauri Tamhankar and Kalpana Malpe
17.1 Introduction
17.2 Background on Diabetes and Traditional Management Approaches
17.3 AI and Machine Learning in Diabetes Care
17.4 Innovations in Continuous Glucose Monitoring (CGM) Systems
17.5 AI for Personalized Insulin Dosing
17.6 Integration of AI in Diabetes Management Platforms
17.7 Challenges and Ethical Considerations
17.8 Conclusion
References
18. Chronic Heart Disease Management with AI: Predictive Models and Early Interventions Using AI to Monitor Heart Disease Patients, Predict Adverse Events, and Recommend Preventive Measures
Patil Dilip P. and Swapna Kamble
18.1 Introduction
18.2 The Role of AI in Monitoring Heart Disease
18.2.1 Connectivity with Cloud-Based Health Records
18.2.2 Technologies for Real-Time Patient Monitoring
18.2.3 Case Studies: AI
18.3 Predictive Models for Adverse Event Forecasting
18.3.1 Predictive Modeling Techniques
18.3.2 AI Models in Predicting Heart Attacks and Heart Failure
18.3.3 Evaluating the Efficacy and Accuracy of Predictive Models
18.4 AI-Driven Preventive Interventions
18.4.1 AI and Personalized Medicine
18.4.2 Lifestyle and Behavioral Interventions
18.5 Discussion
18.6 Challenges in Implementing AI in Healthcare
18.6.1 Data Privacy and Security Concerns
18.6.2 Ethical Considerations and Bias in AI Models
18.6.3 Scalability and Infrastructure Requirements
18.7 Future Directions and Innovations
18.7.1 Emerging Technologies in AI and Heart Disease
18.7.2 Potential for Global Impact and Healthcare System Integration
18.7.3 Collaboration and Multidisciplinary Approaches
18.8 Conclusion
References
Index
Foreword
Preface
19. AI-Assisted Decision Support Systems in Chronic Disease Treatment: The Role of AI in Assisting Clinicians with Diagnosis, Treatment Recommendations, and Patient Management
Makarand B. Mane and Jiwan Dehankar
19.1 Introduction
19.2 Theoretical Background
19.3 AI in Diagnosis
19.3.1 AI Techniques for Data Analysis and Pattern Recognition
19.3.2 Case Studies: AI in Early Detection and Diagnosis of Chronic Diseases
19.3.3 Accuracy and Reliability of AI Diagnostics
19.4 AI in Treatment Recommendation
19.4.1 AI Models for Personalized Treatment Plans
19.4.2 Integration of AI with Clinical Guidelines and Evidence-Based Practices
19.4.3 Examples of AI Systems Improving Treatment Efficacy
19.5 AI in Patient Management
19.5.1 Real-Time Monitoring and Patient Data Analysis
19.5.2 AI-Driven Predictive Models for Patient Management
19.5.3 Role of AI in Enhancing Patient Compliance and Follow-Up Care
19.6 Discussion of Results
19.7 Ethical Considerations and Challenges
19.7.1 Ethical Implications of AI in Healthcare
19.7.2 Data Privacy and Security Concerns
19.7.3 Challenges in Implementation and Clinical Adoption
19.8 Conclusion
References
20. Introduction to AI in Chronic Disease Management Overview of AI Technologies and Their Transformative Impact on Chronic Disease Care
V. C. Patil and G. M. Vaidya
20.1 Introduction
20.2 Related Work
20.3 AI Technologies in Chronic Disease Management
20.4 Impact of AI on Chronic Disease Care
20.5 Challenges and Ethical Considerations
20.6 Results and Discussion
20.7 Conclusion
References
21. Artificial Intelligence–Driven Identification of Biomarkers for Precision Medicine Advancements Through Bioinformatics in Healthcare Applications
Abhinov Thamminaina and Shinde Rutuja P.
21.1 Introduction
21.2 Background and Literature Review
21.3 Artificial Intelligence Techniques in Bioinformatics
21.4 Methodology
21.5 Applications in Healthcare
21.6 Future Directions
21.7 Results and Discussion
21.8 Conclusion
References
22. Real-Time Chronic Disease Management with Smart Devices Integrating Internet-of-Things Technology in Healthcare Applications
S. T. Thorat and Inamdar Sharifnawaj Y.
22.1 Introduction
22.2 Background
22.3 IoT Integration in Healthcare
22.4 Methodology
22.5 Real-Time Monitoring with Smart Devices
22.6 Applications of IoT-Enabled Smart Devices in Chronic Disease Management
22.7 Result and Discussion
22.8 Conclusion
References
23. Future Trends in Wearable Healthcare Technology: Innovations and Emerging Technologies in Wearable Devices
Shantanu Kulkarni and Kalpana Malpe
23.1 Introduction
23.1.1 Overview of Wearable Healthcare Technology
23.1.2 The Role of Wearable Technology in Healthcare
23.2 Advanced Sensors in Wearable Devices
23.2.1 Types of Sensors Used in Wearable Technology
23.2.2 Developments in Sensor Technology
23.2.3 Applications of Advanced Sensors in Healthcare
23.3 Artificial Intelligence and Machine Learning in Wearable Healthcare
23.3.1 Artificial Intelligence and Machine Learning in Smart Devices
23.3.2 Use Cases of AI and ML in Healthcare Monitoring
23.3.3 Future Directions of AI and ML in Wearables
23.4 Miniaturization and Design Innovations
23.4.1 Miniaturization of Wearable Components
23.4.2 Wearable Devices Comfort Enhancements and Improvements
23.5 Materials Science in Wearable Technology
23.5.1 Developments for Flexible and Stretchable Structures
23.5.2 Biocompatible Materials for the Purpose of Tissue Integration
23.5.3 Future Applications of Materials Science in Wearables
23.6 AR and VR in Wearable Healthcare
23.6.1 The Incorporation of AR and VR in Wearable Technologies
23.6.2 Application of AR and VR Technologies in Healthcare
23.6.3 AR and VR Might Change the Way Healthcare is Provided
23.7 Regulatory and Ethical Considerations
23.7.1 Regulatory Landscape for Wearable Healthcare Devices
23.7.2 Future Challenges and Opportunities in Regulation and Ethics
23.8 Conclusion
References
24. Challenges and Opportunities in Real-Time Data Processing: Advancements and Limitations in Real-Time Data Analytics
Ravindra S. Patil and Shyamala Moantri
24.1 Introduction
24.1.1 Real-Time Data Processing
24.1.2 Importance of Real-Time Data Analytics
24.2 Challenges in Real-Time Data Processing
24.2.1 Volume and Velocity of Data
24.2.2 Processing with Low Latency
24.2.3 Data Quality and Integrity
24.3 Advancements in Real-Time Data Processing
24.3.1 Streaming Data Processing Technologies
24.3.2 Optimization for Low Latency
24.3.3 Real-Time Analytics Frameworks
24.4 Limitations of Real-Time Data Processing
24.4.1 Privacy and Security Concerns
24.4.2 Complexity of Implementation and Management
24.5 Opportunities in Real-Time Data Processing
24.5.1 Rapid Decision-Making
24.5.2 Operational Efficiency
24.5.3 Enhanced Customer Experiences
24.6 Case Studies
24.6.1 Real-World Applications of Real-Time Data Processing
24.7 Evolving Technologies and Techniques in Real-Time Data Analytics
24.8 Conclusion
References
25. Overview of AI and Machine Learning Algorithms in Health Monitoring: Dermatological and Infectious Disease Applications of AI in Health Monitoring
Asma A. Hussain and Rasika Chafle
25.1 Introduction
25.1.1 Overview of AI and ML Application in Health Monitoring
25.1.2 Recognizing the Role of AI in Monitoring Dermatological and Infectious Diseases
25.2 Fundamentals of Dermatological and Infectious Diseases
25.2.1 Common Dermatological Diseases
25.2.2 Common Infectious Diseases
25.3 Traditional Methods of Disease Monitoring
25.3.1 Manual Diagnosis by Healthcare Professionals
25.3.2 Laboratory Tests and Diagnostic Procedures
25.4 Role of AI in Dermatological Disease Monitoring
25.4.1 Image Recognition and Classification Algorithms
25.4.2 AI Uses in Diagnosing Skin Disorders
25.4.3 Case Studies and Examples
25.5 Role of AI in Infectious Disease Monitoring
25.5.1 Exploratory Case Study: Google Flu Trends
25.5.2 AI-Driven Surveillance Systems
25.5.3 Case Studies and Examples
25.6 Challenges and Limitations
25.6.1 Data Privacy and Security Challenges
25.6.2 Reliability and Accuracy of AI Algorithms
25.6.3 Legal Issues Concerning AI in Healthcare Diagnostics
25.7 Future Directions and Opportunities
25.7.1 Integrating AI with Other Technologies
25.7.2 AI’s Influence on Public Health Monitoring
25.7.3 Research Trends and Areas for Further Exploration
25.8 Conclusion
References
26. Machine Learning Algorithms in Early Detection of Chronic Diseases Applications of Supervised and Unsupervised Learning for Early Diagnosis and Risk Prediction
Vasant Devkar and Shamla Mantri
26.1 Introduction
26.1.1 Early Detection in Chronic Diseases
26.1.2 Role of Machine Learning in Healthcare
26.1.3 Overview of Supervised and Unsupervised Learning
26.2 Related Work
26.3 Supervised Learning in Early Diagnosis
26.3.1 Classification Algorithms for Disease Detection
26.3.2 Regression Models for Risk Prediction
26.3.3 Feature Engineering and Selection Techniques
26.3.4 Applications in Chronic Disease Diagnosis
26.4 Unsupervised Learning for Disease Risk Prediction
26.4.1 Clustering Techniques for Patient Stratification
26.4.2 Methods for Reducing Dimensionality
26.4.3 Pattern Discovery in Unlabeled Medical Data
26.5 Hybrid Approaches: Combining Supervised and Unsupervised Learning
26.5.1 Ensemble Learning Techniques
26.5.2 Semi-Supervised Learning for Constrained Named Information
26.5.3 Case Studies: Integrated Models for Early Detection
26.6 Challenges and Solutions
26.6.1 Data Quality and Missing Values
26.6.2 Model Interpretability and Explainability
26.6.3 Scalability of Machine Learning Models
26.6.4 Ethical Considerations and Bias Mitigation
26.7 Conclusion
References
27. Intelligent Neurohealth Systems: Revolutionizing Diagnosis and Therapy
Iype Cherian and Sunil Ramrao Yadav
27.1 Introduction
27.2 Technological Foundations of INHS
27.2.1 Machine Learning and Artificial Intelligence
27.2.2 Neuroinformatics and Big Data
27.2.3 Brain-Computer Interfaces
27.2.4 Robotics and Neurorehabilitation
27.3 Applications of INHS
27.3.1 Early Diagnosis and Prognosis
27.3.2 Tailored Intervention Approaches
27.3.3 AI-Based Pharmacogenomics
27.3.4 Optimized AI Deep Brain Stimulation (DBS)
27.3.5 Advanced Techniques of Cognitive and Behavioral Therapy Optimization
27.3.6 Pro-Nas Neurofeedback and Rehabilitation
27.3.7 Remote Observation and Telemedicine
27.3.8 Surgical Assistance and Precision Medicine
27.4 Difficulties and Moral Aspects
27.4.1 Security and Confidentiality of Information
27.4.2 Equity and Justice in AI Models
27.4.3 Acceptance of Patients
27.4.4 Regulatory and Legal Concerns
27.4.5 Examining Attainment of Human Synergies
27.4.6 Ethical Implications of Neurotechnology
27.5 Future Directions
27.5.1 Explainable AI (XAI)
27.5.2 Enhancing Quantum Computing
27.5.3 Development of Neuro-AI Interfaces
27.5.4 AI in Mental Health
27.6 Conclusion
References
28. Cognitive Computing and Neurobiology: A New Era in Brain Health
G. V. Ramdas and Debabrata Sahana
28.1 Introduction
28.2 Understanding Cognitive Computing
28.3 Neurobiology: The Science of the Brain
28.4 Intersection of Cognitive Computing and Neurobiology
28.4.1 Ai in Brain Research
28.4.2 Cognitive Models Inspired by the Brain
28.4.3 Brain-Computer Interfaces (BCIs)
28.5 Applications in Brain Health
28.5.1 Managing Neurodegenerative Disorders
28.5.2 Mental Health Treatment
28.5.3 Neuroprosthetic Devices and Rehabilitation
28.6 Cognitive Enhancement and Brain Training
28.7 Ethical Considerations and Challenges
28.8 Future Prospects
28.9 Conclusion
References
29. Natural Language Processing (NLP) in Chronic Disease Management Utilizing NLP to Extract Critical Information from Medical Records for Improving Chronic Disease Care
Patange Aparna P. and Chandrayani Rokde
29.1 Introduction
29.1.1 Overview of Chronic Disease Management
29.1.2 Role of Information Technology in Healthcare
29.2 Background Work
29.3 Foundations of Natural Language Processing
29.3.1 Core NLP Techniques
29.3.2 Challenges in Applying NLP to Healthcare Data
29.4 Methodology
29.4.1 Design and Approach
29.4.2 Data Collection and Preparation
29.4.3 Algorithms
29.5 Applications of NLP in Chronic Disease Care
29.5.1 Extracting Clinical Information from Electronic Health Records (EHRs)
29.5.2 Patient Monitoring and Follow-Up Care
29.5.3 Predictive Analytics in Chronic Disease Management
29.6 Case Study: NLP in Managing Type 2 Diabetes
29.7 Results and Discussion
29.7.1 Interpretation of Findings
29.7.2 Impact on Patient Care and Outcomes
29.8 Challenges and Limitations
29.8.1 NLP Tool Accuracy and Reliability
29.8.2 Interoperability of Health IT Systems
29.9 Conclusion
References
30. Remote Monitoring and Telehealth Solutions for Chronic Disease Care Integration of AI in Telemedicine for Continuous Patient Monitoring and Real-Time Interventions
Nitin N. Jadhav and P. Bainalwar
30.1 Introduction
30.2 Background and Literature Review
30.3 Telehealth Solutions for Chronic Disease Care
30.4 AI and Continuous Monitoring in Telemedicine
30.5 Real-Time Interventions in Chronic Disease Care
30.6 Results and Discussion
30.7 Conclusion
References
31. Innovative Approaches in Mental Health Intervention Using Wearable Devices: Novel Therapeutic Modalities and Interventions
Iype Cherian and Abhinov Thamminaina
31.1 Introduction
31.1.1 The Use of Wearable Devices to Assist Mental Health
31.1.2 Importance of Novel Therapeutic Modalities and Interventions
31.2 Wearable Technology in Mental Healthcare
31.2.1 Evolution of Wearable Devices in Mental Health
31.2.2 Types of Wearable Devices and Their Functionality
31.3 Monitoring and Assessment
31.3.1 Continuous Monitoring of Physiological and Psychological Data
31.3.2 Use of Data Analytics for Mental Health Assessment
31.4 Therapeutic Modalities Enabled by Wearable Devices
31.4.1 Biofeedback and the Control of Stress
31.4.2 Cognitive-Behavioral Interventions
31.4.3 VR Exposure Therapy
31.5 Personalized Interventions
31.5.1 Tailoring Interventions to Individual Needs
31.5.2 Adaptive Feedback and Support Systems
31.6 Challenges and Considerations
31.6.1 Data Privacy and Security
31.6.1.1 Importance of Data Privacy and Security
31.6.1.2 Issues Related to Data Privacy and Security
31.6.1.3 Methods for Mitigating Risks
31.6.2 Integration into Clinical Practice
31.6.2.1 Benefits of Integration
31.6.2.2 Techniques for Merging Various Methods
31.6.3 User Acceptance and Feedback Engagement
31.6.3.1 Importance of User Acceptance and Feeling of Engagement
31.6.3.2 Factors Influencing User Acceptance and Engagement
31.6.3.3 Methods to Improve User Acceptance
31.7 Future Directions
31.7.1 Possible Innovations in the Field of Wearable Technology
31.7.2 Implications for Mental Healthcare Delivery
31.7.2.1 Wearable Technology in Mental Healthcare and Its Benefits
31.7.2.2 Challenges and Issues to Consider
31.7.2.3 Problem in Focus: Elements of Strategy
31.8 Conclusion
References
32. Artificial Intelligence–Driven Identification of Biomarkers for Precision Medicine Advancements Through Bioinformatics in Healthcare Applications
Vasant Devkar and Patil Ashish N.
32.1 Introduction
32.2 Background and Evolution of Telemedicine
32.3 Augmented Reality Technology
32.4 Methodology
32.5 Augmented Reality in Remote Surgical Assistance
32.6 Future Prospects and Challenges
32.7 Results and Discussion
32.8 Conclusion
References
33. Future Directions and Opportunities in AI-Driven Healthcare: Family Medicine and Anesthesiology Future Directions in AI-Driven Healthcare
N.V. Kanase and Shraddha Naik
33.1 Introduction
33.1.1 Overview of AI in Healthcare
33.1.2 Scope of the Chapter
33.2 AI Applications in Family Medicine
33.2.1 EHR Management
33.2.2 Patient Triage and Risk Stratification
33.2.3 Chronic Disease Management
33.2.4 Telemedicine and Remote Monitoring
33.2.5 Patient Participation and Instruction
33.3 AI Applications in Anesthesiology
33.3.1 Preoperative Assessment and Planning
33.3.2 Monitoring During Surgery and Helping with Decisions
33.3.3 Postoperative Care and Recovery
33.3.4 Anesthesia Drug Dosing and Management
33.4 Current Challenges and Limitations
33.4.1 Data Quality and Accessibility
33.4.2 Legal and Ethical Considerations
33.4.3 Integration with Existing Healthcare Systems
33.4.4 Patient Privacy and Consent
33.5 Future Directions and Opportunities
33.5.1 Developments in Machine Learning and Deep Learning
33.5.2 Enhanced Clinical Decision Support Systems
33.5.3 Personalized Medicine and Treatment Planning
33.5.4 AI-Driven Research and Drug Discovery
33.6 Case Studies and Examples
33.6.1 Exemplary Incorporation of AI in Family Medicine
33.6.2 New Ways AI is Being Used in Anesthesiology
33.7 Conclusion
References
34. Future Directions in AI-Powered Medical Diagnostics: Innovations and Challenges in AI-Driven Diagnostic Technologies
Hemanth Kumar R. G. and Radhika Chintamani
34.1 Introduction
34.1.1 AI-Powered Medical Diagnostics
34.1.2 AI Importance in Healthcare
34.2 Current Landscape of AI in Medical Diagnostics
34.2.1 Evolution of AI in Healthcare
34.2.2 AI Uses in Medical Diagnostics
34.2.3 Benefits of AI in Improving Diagnostic Accuracy
34.3 Innovations in AI-Driven Diagnostic Technologies
34.3.1 Integration of AI with Wearable Devices for Continuous Monitoring
34.3.2 Real-Time Analysis of Biological Markers Using AI
34.3.3 AI Algorithm Innovations in Medical Imaging Interpretation
34.4 Challenges in AI-Driven Medical Diagnostics
34.4.1 Absence of Comprehensive Datasets to Train AI Algorithms
34.4.2 Variability in Performance Across Different Populations and Settings
34.4.3 Issues of Privacy and Security in the Context of AI-Powered Diagnostic Systems
34.5 Future Directions in AI-Powered Medical Diagnostics
34.5.1 Collaborative Approaches for the Creation of Uniform Datasets
34.5.2 Guidelines for AI Algorithm Development and Deployment
34.5.3 The Policies and Measures to Guarantee the Protection of Patients’ Privacy and Confidentiality
34.6 Case Studies and Applications
34.6.1 Case Studies Showcasing Successful Implementation of AI in Medical Diagnostics
34.6.2 Applications of AI in Radiology and Pathology AI
34.7 Recommendations for Further Research and Development
34.8 Conclusion
References
35. AI in Cancer Screening and Early Detection
Kumud Sachdeva and Rajan Sachdeva
35.1 Introduction
35.1.1 Cancer
35.1.2 Risk Factors for Cancer
35.1.3 Percentage of People Survive Cancer
35.1.4 Blood Cancer
35.1.5 Role of Deep Learning to Detect Cancer
35.2 Literature Survey
35.2.1 Introduction
35.2.2 Methodology for Literature Review
35.2.3 Deep Learning Analysis
35.2.4 Convolution Neural Network
35.3 Performance Analysis of Machine Learning Techniques and Deep Learning Methods for Pancreatic Cancer Diagnosis in e-Health Care System
35.4 Hybrid Method
35.5 Classification of Cancer Database Images
35.6 Conclusion and Future Scope
References
36. The Rhizobium-Legume Symbiosis and Biofortification in Sustainable Agriculture
Abhay Ashok Ghatage
36.1 Introduction
36.1.1 The Nutritional Deficit
36.1.2 Progressive Cultivation Practices
36.1.3 The Role of Legumes
36.2 Role of Legumes in Human Nutrition
36.2.1 Nutritional Importance of Peas, Mung Beans, and Soybean
36.2.1.1 Peas
36.2.1.2 Mung Bean
36.2.1.3 Soyabean
36.3 Biofortification of Legumes: Techniques Involved in Nutrition Enhancement
36.3.1 Conventional Breeding
36.3.2 Agronomic Interventions
36.3.3 Transgenic Methods
36.4 Country-Wise Use of Nitrogen Fertilizer
36.5 Evolutionary History of Legumes
36.5.1 Divergence and Domestication of Food Legumes
36.5.1.1 Vigna spp.
36.5.1.2 Glycine max (Soyabean)
36.5.1.3 Pea (Pisum sativum)
36.5.1.4 Cicer arietinum (Chickpea)
36.5.1.5 Common Bean (Phaseolus vulgaris L)
36.5.1.6 Lens culinaris Medik. (Lentil)
36.5.2 Rhizobium
36.6 Biological Nitrogen Fixation and Its Benefits
36.7 Conclusion
References
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