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Submit a ProposalInternet of Medicine for Smart Healthcare
One Line Description
This book provides in-depth explanations and discussions of the latest applications of AI, machine learning, and the Internet of Medicine, offering readers the cutting edge on this rapidly growing technology that has the potential to transform healthcare and improve patient outcomes.
Audience
Students, educators, researchers, medical device manufacturers, software developers, healthcare providers, and pharmaceutical companies interested in the use of the Internet of Medicine
Students, educators, researchers, medical device manufacturers, software developers, healthcare providers, and pharmaceutical companies interested in the use of the Internet of Medicine
Description
Over the past five years, there have been significant advances in healthcare through the use of artificial intelligence (AI) and machine learning (ML) technologies. AI and machine learning in medical imaging has significantly improved the accuracy and speed of medical imaging analysis, accelerated the drug discovery process by identifying potential drug targets and predicting the efficacy and safety of new drugs, and enabled personalized medicine by analyzing large amounts of patient data to identify individualized treatment plans based on a patients genetic makeup and medical history. Internet of Medicine (IoM) refers to the integration of the Internet of Things (IoT) and connected medical devices with healthcare systems and processes to enable remote monitoring, diagnosis, and treatment of patients. IoM is a subset of the larger Internet of Things (concept, which involves the connection of everyday devices and appliances to the internet for various purposes. IoM has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and increasing efficiency. Some of the specific applications of IoM include remote patient monitoring, real-time data analysis, predictive analytics, smart hospitals, and personalized medicine.
Overall, the Internet of Medicine is a rapidly growing field that has the potential to transform healthcare and improve patient outcomes. However, there are also concerns about data privacy and security, as well as the need for regulatory standards and guidelines to ensure the safe and effective use of connected medical devices. The future of IoM is likely to see increased integration and interoperability, enabling personalized and efficient healthcare delivery, and the integration of emerging technologies such as AI and blockchain. Addressing these challenges will require a collaborative effort between healthcare providers, policymakers, and technology companies to ensure that the benefits of intelligent healthcare are accessible to all patients while protecting their privacy and rights. The application of AI for this topic involves the use of machine learning algorithms to analyze large amounts of healthcare data and provide personalized insights and recommendations to patients and healthcare providers. Internet of Medicine for Smart Healthcare offers in-depth explanations and discussions of applications to give readers the cutting edge on this growing technology.
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Over the past five years, there have been significant advances in healthcare through the use of artificial intelligence (AI) and machine learning (ML) technologies. AI and machine learning in medical imaging has significantly improved the accuracy and speed of medical imaging analysis, accelerated the drug discovery process by identifying potential drug targets and predicting the efficacy and safety of new drugs, and enabled personalized medicine by analyzing large amounts of patient data to identify individualized treatment plans based on a patients genetic makeup and medical history. Internet of Medicine (IoM) refers to the integration of the Internet of Things (IoT) and connected medical devices with healthcare systems and processes to enable remote monitoring, diagnosis, and treatment of patients. IoM is a subset of the larger Internet of Things (concept, which involves the connection of everyday devices and appliances to the internet for various purposes. IoM has the potential to revolutionize healthcare by improving patient outcomes, reducing costs, and increasing efficiency. Some of the specific applications of IoM include remote patient monitoring, real-time data analysis, predictive analytics, smart hospitals, and personalized medicine.
Overall, the Internet of Medicine is a rapidly growing field that has the potential to transform healthcare and improve patient outcomes. However, there are also concerns about data privacy and security, as well as the need for regulatory standards and guidelines to ensure the safe and effective use of connected medical devices. The future of IoM is likely to see increased integration and interoperability, enabling personalized and efficient healthcare delivery, and the integration of emerging technologies such as AI and blockchain. Addressing these challenges will require a collaborative effort between healthcare providers, policymakers, and technology companies to ensure that the benefits of intelligent healthcare are accessible to all patients while protecting their privacy and rights. The application of AI for this topic involves the use of machine learning algorithms to analyze large amounts of healthcare data and provide personalized insights and recommendations to patients and healthcare providers. Internet of Medicine for Smart Healthcare offers in-depth explanations and discussions of applications to give readers the cutting edge on this growing technology.
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Author / Editor Details
Abhishek Kumar, PhD is currently working as an Assistant Director/Associate professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India. He holds a Doctorate in computer science from the University of Madras. He is a Post-Doctoral Fellow in Ingenium Research Group Ingenium Research Group Lab, Universidad De Castilla-La Mancha, Ciudad Real, and Ciudad Real Spain. He has an MTech in Computer Science & Engineering, and a B.Tech in I.T. from Rajasthan Technical University, Kota, India. He has a total Academic teaching experience of more than 13 years, along with 2 years of teaching assistantship. He has more than 170 publications in reputed, peer-reviewed National and International Journals, books, & Conferences. He has guided more than 30 MTech Projects at national and international levels, and 4 PhD scholars completed their Degrees under his Guidance. His research areas include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning. He has been session chair and keynote speaker at many international conferences and webinars in India and abroad. He has been a reviewer for IEEE and Inderscience Journal. He has authored/Co-Authored 7 books published internationally and edited 63 books (Published & ongoing with IET, Elsevier, Wiley, IGI GLOBAL Springer, Apple Academic Press, De Gruyter and CRC etc. He serves as Series Editor for four books series: Quantum Computing with De Gruyter, Germany, Intelligent Energy System with Elsevier, & Mathematical Methods in the Digital Age with De Gruyter, Germany, and AI for Environment Sustainability with De Gruyter, Germany.
Abhishek Kumar, PhD is currently working as an Assistant Director/Associate professor in the Computer Science & Engineering Department at Chandigarh University, Punjab, India. He holds a Doctorate in computer science from the University of Madras. He is a Post-Doctoral Fellow in Ingenium Research Group Ingenium Research Group Lab, Universidad De Castilla-La Mancha, Ciudad Real, and Ciudad Real Spain. He has an MTech in Computer Science & Engineering, and a B.Tech in I.T. from Rajasthan Technical University, Kota, India. He has a total Academic teaching experience of more than 13 years, along with 2 years of teaching assistantship. He has more than 170 publications in reputed, peer-reviewed National and International Journals, books, & Conferences. He has guided more than 30 MTech Projects at national and international levels, and 4 PhD scholars completed their Degrees under his Guidance. His research areas include artificial intelligence, renewable energy image processing, computer vision, data mining, and machine learning. He has been session chair and keynote speaker at many international conferences and webinars in India and abroad. He has been a reviewer for IEEE and Inderscience Journal. He has authored/Co-Authored 7 books published internationally and edited 63 books (Published & ongoing with IET, Elsevier, Wiley, IGI GLOBAL Springer, Apple Academic Press, De Gruyter and CRC etc. He serves as Series Editor for four books series: Quantum Computing with De Gruyter, Germany, Intelligent Energy System with Elsevier, & Mathematical Methods in the Digital Age with De Gruyter, Germany, and AI for Environment Sustainability with De Gruyter, Germany.
Narayan Vyas is an Assistant Professor in the Department of Computer Science and Application at Vivekananda Global University, Jaipur, India, where he is actively involved in research and development in computer science. He qualified for the NTA UGC NET & JRF on his first attempt, showcasing his academic excellence. He has extensive knowledge of the Internet of Things and Mobile Application Development and has provided training to students worldwide. He has published numerous articles in reputed, peer-reviewed national and international Scopus-indexed conferences and journals. Additionally, he has served as a keynote speaker and resource person for several workshops and webinars conducted in India. His research areas include Remote Sensing, the Internet of Things, Machine Learning, Deep Learning, and Computer Vision. He is an IEEE member and an active member of various international and national societies, such as IEEE Young Professionals, the Indian Society of Remote Sensing (ISRS), the IEEE Geosciences and Remote Sensing Society (GRSS), the IEEE Sensors Council, ACM, and the International Society of Photogrammetry and Remote Sensing (ISPRS). He also received the International Distinguished Young Researcher Award from the ASET Journal of Management Science. He has edited more than 12 books with various reputable publishers, including Wiley, De Gruyter, Apple Academic Press, and IGI Global.
Pramod Singh Rathore, PhD is currently working as an Assistant professor of Department of Computer and Communication Engineering, Manipal University Jaipur. He is Doctorate in Computer Science & Engineering from University of Engineering and Management, Jaipur (UEM) and done M. Tech in Computer Sci. & Engineering from Government engineering college Ajmer, Rajasthan Technical University, Kota India. With over 12 years of academic teaching experience, he has published more than 85 papers in reputable, peer-reviewed national and international journals, books, and conferences, including Wiley, IGI GLOBAL, Taylor & Francis Springer, Elsevier Science Direct, Annals of Computer Science, Poland, and IEEE. He has co-authored and edited numerous books with well-known publishers such as Wiley, CRC Press, and USA. His research interests include NS2, computer networks, mining, and DBMS. He serves on the editorial and advisory committees of the Global Journal Group and is also a member of various national and international professional societies in the field of engineering and research, including the ACM, the International Association of Engineers (IAENG).
Abhineet Anand, PhD brings over 24 years of academic and administrative expertise to his role as Dean at Bahra University, Shimla Hills. His research spans key areas, including Cloud Computing, Cloud Security, Decision Trees, Nearest Neighbor Methods, Clustering, Rule Induction, Optical Fiber Switching in Wavelength Multiplexing, and Automata Theory. Research and Publications: Dr. Anand has a significant body of work, with 8 Book Editor roles, over 65 Scopus-indexed papers, 14 SCI-indexed papers, 16 papers in International Conferences, and 12 papers in International Journals. Academic Contributions: He has chaired or co-chaired multiple special sessions at international conferences and has been an active Technical Program Committee member for 20 conferences. His leadership and extensive research contribute to his reputation as a leading figure in the academic community.
Pooja Dixit is an Assistant Professor in the Department of Computer Science at Sophia Girls’ College (Autonomous), Ajmer, India, and she is pursuing her Ph.D. in Computer Science at Manipal University Jaipur. With over seven years of academic teaching and two years of research experience, she has published 25+ research papers in reputed journals, books, and conferences. Her research interests include Artificial Intelligence, Machine Learning, and Data Mining. Pooja is a member of various professional societies, including IAENG, IRED, IAIP, ISRD, and ISOC, and serves on the editorial board of IOSRD. She has also edited a book published by Wiley. She holds an MCA and BCA from MDS University, Ajmer, India.
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Table of Contents
Preface
1. Omics Data Integration in AI System for Immediate and Carryover Effects of Neurodynamic Exercises on SLR Ranges Among Acute PIVD Patients
Durga Bahuguna, Vaibhav Agarwal and Manish Kumar Jha
1.1 Introduction
1.2 Literature Review
1.3 Methodology
1.3.1 Research Question
1.3.2 Aim of the Study
1.3.3 Need of the Study
1.4 Hypothesis
1.4.1 Alternate Hypothesis
1.4.2 Null Hypothesis
1.4.3 Study Design
1.4.4 Study Center
1.4.5 Study Population
1.4.6 Sampling
1.4.7 Study Duration
1.4.8 Sample Size
1.4.9 Inclusion Criteria
1.4.10 Exclusion Criteria
1.4.11 Outcome Variables
1.4.12 Instrumentation
1.5 Procedure
1.6 Intervention
1.6.1 Four Levels of Neurodynamics
1.6.2 Control Group Intervention
1.7 Data Analysis
1.8 Result
1.9 Discussion
1.10 Conclusion
References
2. Effectiveness of Graph-Based Methods for Biological Networks for Primal Reflex Release Techniques on Pain and Disability in Cervicogenic Headache Patient
Siddhartha Kuriya, Manish Kumar Jha, Vaibhav Agarwal and Amit Sharma
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.3.1 Need of the Study
2.3.2 Aim of the Study
2.3.3 Hypothesis
2.3.3.1 Experimental Hypothesis
2.3.3.2 Null Hypothesis
2.3.4 Protocol
2.4.1 Procedure
2.4.2 OMNE
2.4.3 Clinical Relevance
2.4.4 Limitation of the Study
2.4.5 Scope of Future Research
2.5 Conclusion
References
3. Application of AI in Determining Immediate and Carryover Effects of Primal Reflex Release Technique Neural Reboot on SI Joint Mobility
Rakesh Chaudhary, Vaibhav Agarwal and Aashish Negi
3.1 Introduction
3.2 Literature Review
3.3 Mehodology
3.3.1 Aim of Study
3.3.2 Need of the Study
3.3.3 Hypothesis
3.3.3.1 Null Hypothesis
3.3.3.2 Alternate Hypothesis
3.3.4 Operational Definition
3.3.4.1 Desk Job Worker
3.3.5 Data Analysis
3.3.6 Protocol
3.3.7 Procedure
3.4 Results
3.4.1 Gender and Age Frequency
3.4.2 Experimental and Control Group Scores
3.4.3 Clinical Implication
3.4.4 Future Research
3.4.5 Limitation
3.5 Conclusion
References
4. Future Trends in Bioinformatics AI Integration for Analyzing Immediate Effect of Primal Reflex Release Technique in Neck Pain and Stiffness Patients
Aditya Sharma, Manish Kumar Jha, Vaibhav Agarwal and Amit Sharma
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.3.1 Need of Study
4.3.2 Aim of the Study
4.3.3 Experimental Hypothesis
4.3.3.1 Null Hypothesis
4.3.3.2 Instrumentation
4.3.4 Inclusion Criteria
4.3.5 Exclusion Criteria
4.3.6 Protocol
4.3.7 Procedure
4.3.8 Statistical Analysis
4.3.9 Distribution of Study Subjects
4.4 Conclusion
References
5. Evolutionary Computation in Bioinformatics Analyzing the Effects of Neurodynamic Exercises on Pain and Disability in Carpal Tunnel Syndrome Patients
Manish Pathak, Vaibhav Agarwal and Manish Kumar Jha
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.3.1 Objectives
5.3.2 Hypothesis
5.3.2.1 Null Hypothesis
5.3.2.2 Experimental Hypothesis
5.3.3 Aim of Study
5.3.3.1 Study Design
5.3.3.2 Study Center
5.3.3.3 Study Population
5.3.3.4 Sample Method
5.3.3.5 Sample Size
5.3.4 Procedure
5.3.4.1 Protocol
5.3.5 Experimental Group Intervention
5.3.5.1 Exercises
5.3.5.2 Exercise Methods
5.3.6 Data Analysis
5.4 Result and Discussion
5.5 Conclusion
References
6. Imperative Role of Artificial Intelligence and Nanotechnology in Healthcare Sector for Sustainable Development
Sheela Bijlwan
6.1 Introduction
6.2 Literature Review
6.3 AI Applications in Healthcare Industries
6.4 Application of AI and Nanotechnology in Medicine
6.5 AI and Nanotechnology in Anti-Aging Medicines
6.6 Result
6.7 Discussion
6.8 Conclusion
References
7. Holistic Approaches for Sleep Pattern Enhancement Using AI and Yoga Therapy: A Comprehensive Scientific Approach
Somlata Jha and Siddhant Rajhans
7.1 Introduction
7.2 Understanding Sleep Patterns
7.3 The Importance of Quality Sleep
7.4 The Role of AI in Sleep Pattern Enhancement
7.5 How AI Works to Enhance Sleep
7.6 How Yoga Influences Sleep
7.7 Combining AI and Yoga for Better Sleep
7.8 The Synergy of AI and Yoga
7.9 Scientific Research on Sleep Improvement
7.10 The Mind–Body Connection
7.11 A Holistic Approach to Sleep
7.12 Practicing Yoga for Better Sleep
7.13 AI-Enabled Sleep Tracking Devices
7.14 Personalized Sleep Plans
7.15 Lifestyle Factors and Sleep
7.16 Conclusion
References
8. Ethical Consideration in Bioinformatics in AI for Analyzing the Effects of Using SHAT Device on Upper Extremity Functions in Stroke Patients
Kapil Lakhwara, Vaibhav Agarwal and Manish Kumar Jha
8.1 Introduction
8.2 SHAT Device (Synchronized Hand Arm Training Device)
8.3 How to Perform SHAT Exercises
8.4 Literature Review
8.5 Methodology
8.5.1 Need of the Study
8.5.2 Aim of the Study
8.5.3 Experimental Hypothesis
8.5.3.1 Null Hypothesis
8.5.4 Study Design
8.5.4.1 Inclusion Criteria
8.5.4.2 Exclusion Criteria
8.6 Protocol
8.7 Procedure
8.8 Results
8.8.1 Gender and Age Frequency
8.8.2 Control Group Scores
8.8.3 Experimental Group Scores
8.8.4 Comparison of Means
8.9 Discussion
8.9.1 Clinical Implication
8.9.2 Future Research
8.9.3 Limitation
8.10 Conclusion
References
9. AI-Driven Drug Discovery and Repurposing for Analyzing Long-Term Effects of Nerve Sliders and Tensioners on Quality of Life in Cervicogenic Headache Patients
Shashwat Pandya, Manish Kumar Jha, Vaibhav Agarwal and Amit Sharma
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.3.1 Aim of the Study
9.3.2 Need of the Study
9.3.3 Hypothesis
9.3.3.1 Experimental Hypothesis
9.3.3.2 Null Hypothesis
9.4 Protocol
9.5 Procedure
9.6 Discussion
9.7 Conclusion
References
10. Using Artificial Intelligence (AI) Analyzing Recent Advancements in the Anti-Cancerous Properties of Edible Mushrooms and Their Association with the Mode of Action of Polysaccharides
Vishal Rajput, Manish Tenguria, Sanjay Gupta, Neha Sharma and Smriti Rai
10.1 Introduction
10.1.1 Composition of Polysaccharides Derived from Ganoderma
10.2 Polysaccharide Metabolism and Bioavailability
10.2.1 Preventive Measures of Cancer Cell Migration and Proliferation
10.2.2 Pathways Controlling Cell Death
10.2.2.1 Augmentation of Therapeutic Sensitivity
10.3 Conclusion
References
11. Impact of Artificial Intelligence (AI) in Bioremediation of Dairy Effluent by Microalgae and the Potential Application of the Produced Lipid Byproducts
Nisha Dhillon, Vivek Kumar, Geeta Bhandari and Sanjay Gupta
11.1 Introduction
11.1.1 Type of Wastes While Dairy Processing
11.1.2 Technologies Used for the Treatment of Dairy Effluent
11.2 Microalgae
11.3 Lipid-Producing Microalgal Strains
11.4 Biosynthesis of Lipid in Microalgae
11.5 Applications of Microalgal Lipids
11.5.1 In Food and Nutrition
11.5.2 In the Field of Pharmaceuticals
11.5.3 In the Field of Biofuel
11.5.3.1 As Biodiesel
11.5.3.2 As Biohydrogen
11.6 Challenges in the Field of Microalgal Biomass Productivity
11.7 Conclusion
References
12. Smart Collision Recognition and Reporting System with GPS and GSM Integration
Gaurav Aggarwal, Pooja Joshi and Ashutosh Bhatt
12.1 Introduction
12.2 Literature Review
12.3 Block Diagram
12.4 Methodology
12.4.1 Arduino Uno
12.4.2 GSM Module
12.4.3 GPS Module
12.4.4 Shock Sensor
12.4.5 Temperature Sensor
12.4.6 Automobile Collision Reporting System
12.5 Conclusion
References
13. Evolution and Impact of Wearable Devices in Healthcare: Anatomy of Wearable Technology and its Influence on Medical Sciences
M. P. Ambali and V. C. Patil
13.1 Introduction
13.1.1 Wearable Technology in Healthcare
13.1.2 Purpose and Objectives of the Chapter
13.2 Historical Development of Wearable Technology
13.2.1 Early Wearable Devices in Healthcare
13.2.2 Technological Advancements Leading to Modern Wearable Devices
13.3 Anatomy of Wearable Technology
13.3.1 Sensor Technologies in Wearable Devices
13.3.2 Connectivity and Data Transmission
13.3.3 Power Management and Battery Life
13.3.4 Design and Ergonomics
13.4 Types of Wearable Devices in Healthcare
13.4.1 Trackers for Fitness
13.4.2 Wearable Technology
13.4.3 Devices for Medical Monitoring
13.4.4 Wearable Drug Delivery Systems
13.5 Applications of Wearable Technology in Medical Sciences
13.5.1 Remote Patient Monitoring
13.5.2 Chronic Disease Management
13.5.3 Rehabilitation and Physical Therapy
13.5.4 Health and Wellness Promotion
13.6 Impact of Wearable Technology on Healthcare
13.6.1 Improved Patient Outcomes
13.6.2 Enhanced Doctor–Patient Communication
13.6.3 Cost-Effectiveness and Efficiency in Healthcare Delivery
13.6.4 Challenges and Limitations
13.7 Future Trends and Innovations in Wearable Technology
13.7.1 Artificial Intelligence and Machine Learning in Wearable Devices
13.7.2 Integrating Wearable Devices With Electronic Health Records
13.7.3 Ethical and Legal Considerations in Wearable Technology
13.8 Conclusion
References
14. Current State of Wearable Healthcare Technology: Physiology and Biochemistry of Wearable Sensors and Devices
Jyotsna A. Patil and Shrirang N. Patil
14.1 Introduction
14.1.1 Overview of Wearable Healthcare Technology
14.1.2 Importance of Monitoring Physiology and Biochemistry
14.2 Physiological Monitoring
14.2.1 Heart Rate Monitoring
14.2.2 Body Temperature Monitoring
14.2.3 Blood Pressure Monitoring
14.2.4 Respiratory Rate Monitoring
14.2.5 Activity and Sleep Monitoring
14.3 Biochemical Monitoring
14.3.1 Glucose Monitoring
14.3.2 Lactate Monitoring
14.3.3 Cortisol Monitoring
14.3.4 Other Biochemical Markers
14.4 Integration of Sensors and Devices
14.4.1 Wearable Sensor Technologies
14.4.2 Integration of Sensors into Wearable Devices
14.4.3 Data Transmission and Connectivity
14.5 Applications in Healthcare
14.5.1 Chronic Disease Management
14.5.2 Monitoring for Fitness and Sports
14.5.3 Stress Monitoring
14.5.4 Remote Patient Monitoring
14.6 Advances in Data Analytics and Artificial Intelligence
14.6.1 Machine Learning and Predictive Analytics
14.6.2 Real-Time Health Monitoring and Alerts
14.6.3 Personalized Healthcare and Treatment Planning
14.6.4 Data Security and Privacy
14.7 Challenges and Future Directions
14.7.1 Data Privacy and Security
14.7.2 Reliability and Accuracy of Wearable Devices
14.7.3 Interoperability with Healthcare Systems
14.7.4 Future Trends in Wearable Healthcare Technology
14.8 Conclusion
References
15. Real-Time Data Acquisition and Analysis Techniques: Microbiological and Immunological Aspects of Real-Time Health Data Collection
Satish R. Patil and Supriya S. Patil
15.1 Introduction
15.1.1 Overview of Real-Time Data Acquisition and Analysis Techniques
15.1.2 Importance of Microbiological and Immunological Data in Healthcare
15.2 Real-Time Microbiological Data Collection
15.2.1 Monitoring Microbial Populations in Healthcare Settings
15.2.2 Detection of Infectious Diseases and Antimicrobial Resistance
15.2.3 Applications in Infection Control and Outbreak Prevention
15.3 Real-Time Immunological Data Collection
15.3.1 Monitoring Immune Response to Pathogens and Vaccines
15.3.2 Use in Tracking Disease Progression and Predicting Immune-Related Disorders
15.3.3 Clinical Applications in Personalized Medicine and Patient Care
15.4 Technology and Tools for Real-Time Data Collection
15.4.1 Sensors and Devices for Microbiological and Immunological Data
15.4.2 Data Acquisition and Transmission Protocols
15.4.3 Integration with Existing Healthcare Systems
15.5 Data Analysis Techniques
15.5.1 Statistical Analysis of Real-Time Microbiological and Immunological Data
15.5.2 Machine Learning and AI Approaches for Pattern Recognition and Prediction
15.5.3 Visualization Techniques for Interpreting Complex Data
15.6 Case Studies and Applications
15.6.1 Real-World Examples of Real-Time Data Collection and Analysis in Healthcare
15.6.2 Impact on Patient Outcomes and Healthcare Efficiency
15.7 Future Directions and Challenges
15.7.1 Emerging Technologies and Trends in Real-Time Data Collection
15.7.2 Ethical and Regulatory Considerations
15.7.3 Challenges in Implementation and Adoption
15.8 Conclusion
References
16. Applications of Real-Time Data in Healthcare Interventions: Pharmacological Interventions Based on Real-Time Data Analytics
V. M. Thorat and Satish V. Kakade
16.1 Introduction
16.1.1 Overview of Real-Time Data Analytics in Healthcare
16.1.1.1 Real-Time Monitoring and Alerts
16.1.1.2 Customized Healthcare
16.1.1.3 Analytics for Prediction
16.1.1.4 Managing the Health of a Population
16.1.2 Importance of Real-Time Data in Pharmacological Interventions
16.2 Personalized Medicine
16.2.1 Role of Real-Time Data in Personalized Medicine
16.2.2 Examples of Personalized Medicine in Pharmacological Interventions
16.2.3 Benefits and Challenges of Personalized Medicine
16.3 Treatment Efficacy
16.3.1 Enhancing Treatment Efficacy Through Real-Time Data Analytics
16.3.2 Case Studies Demonstrating Improved Treatment Outcomes
16.3.3 Strategies for Optimizing Treatment Efficacy Using Real-Time Data
16.4 Proactive Interventions
16.4.1 Real-Time Monitoring for Proactive Interventions
16.4.2 Examples of Proactive Interventions in Chronic Disease Management
16.4.3 Impact on Patient Outcomes and Healthcare Costs
16.5 Clinical Trials
16.5.1 Real-Time Data Analytics in Clinical Trial Design
16.5.2 Adaptive Trial Designs Enabled by Real-Time Data
16.5.3 Accelerating Drug Development with Real-Time Data Analytics
16.6 Challenges and Future Directions
16.6.1 Ethical Considerations in Real-Time Data Analytics
16.6.2 Addressing Data Privacy and Security Concerns
16.6.3 Future Trends and Innovations in Real-Time Data Analytics for Pharmacological Interventions
16.7 Conclusion
References
17. Strategies for Integrating Wearable Technology with Healthcare Systems: Pathological Considerations in Wearable Device Integration
Sujata Raghunath Kanetkar and Vaishali V. Raje
17.1 Introduction
17.1.1 Overview of Wearable Technology in Healthcare
17.1.2 Importance of Integrating Wearable Devices with Healthcare Systems
17.2 Pathological Considerations in Wearable Device Integration
17.2.1 Challenges in Collecting and Interpreting Pathological Data
17.2.2 Specialized Sensors and Algorithms for Pathological Data
17.2.3 Interoperability with Existing Healthcare Systems
17.3 Strategies for Effective Integration
17.3.1 Developing Standards for Data Exchange
17.3.2 Implementing Robust Security Measures
17.3.3 Ensuring Seamless Integration with Electronic Health Records (EHRs)
17.3.4 Enhancing User Engagement and Compliance
17.4 Case Studies and Examples
17.4.1 Successful Integration of Wearable Technology in Pathological Monitoring
17.4.2 Lessons Learned and Best Practices
17.5 Future Directions and Emerging Trends
17.5.1 Advances in Sensor Technology for Pathological Monitoring
17.5.2 Integration with Artificial Intelligence and Machine Learning
17.5.3 Regulatory Considerations and Policy Implications
17.6 Conclusion
References
18. Challenges and Solutions in Healthcare System Integration: Histological Perspectives on Wearable Device Integration Challenges
Yugantara R. Kadam and Sujata Raghunath Kanetkar
18.1 Introduction
18.1.1 Overview of Wearable Devices in Healthcare
18.1.2 Importance of Integration in Healthcare Systems
18.2 Challenges in Integrating Wearable Devices
18.2.1 Interoperability Issues
18.2.2 Security and Privacy Concerns
18.2.3 Accuracy and Reliability of Data
18.2.4 Scalability and Sustainability
18.3 Interoperability Challenges
18.3.1 Different Data Formats and Standards
18.3.2 Integration with Electronic Health Records (EHRs)
18.4 Security and Privacy Concerns
18.4.1 Data Encryption
18.4.2 Authorization and Access Control
18.4.3 Compliance with Regulations (e.g., HIPAA)
18.5 Accuracy and Reliability of Data
18.5.1 Sensor Technology
18.5.2 Data Calibration
18.5.3 User Compliance and Education
18.6 Scalability and Sustainability
18.6.1 Cloud-Based Solutions
18.6.2 Needs for Infrastructure
18.6.3 Cost Considerations
18.7 Solutions to Integration Challenges
18.7.1 Development and Adoption of Standards
18.7.2 Enhanced Security Measures
18.7.3 Improvements in Sensor Technology
18.7.4 Cloud-Based Storage and Processing Solutions
18.8 Case Studies and Best Practices
18.8.1 Successful Integration Examples
18.8.2 Lessons Learned
18.8.3 Recommendations for Healthcare Systems
18.9 Future Trends and Outlook
18.9.1 Advancements in Wearable Technology
18.9.2 Integration Challenges and Opportunities
18.9.3 Implications for Healthcare Delivery
18.10 Conclusion
References
19. Role of Wearable Technology in Mental Health Monitoring and Management: Psychiatric Insights into Wearable Technology Adoption
Sharad V. Kshirsagar and V. C. Patil
19.1 Introduction
19.1.1 Overview of Wearable Technology in Mental Health Monitoring and Management
19.1.2 Importance of Psychiatric Insights in Wearable Technology Adoption
19.2 Adoption of Wearable Technology in Mental Healthcare
19.2.1 Things that Affect Adoption
19.2.2 Psychiatric Perspectives on User Acceptance
19.2.3 Design Considerations for User-Friendly Wearable Devices
19.3 Data Privacy and Security in Mental Healthcare
19.3.1 Importance of Data Privacy in Mental Health Monitoring
19.3.2 Psychiatric Insights into Secure Data Management Systems
19.3.3 Regulatory Compliance in Wearable Technology Adoption
19.4 Potential Impact of Wearable Technology in Mental Healthcare
19.4.1 Early Detection of Symptoms
19.4.2 Personalized Interventions
19.4.3 Patient Engagement and Self-Management
19.5 Case Studies and Examples
19.5.1 Successful Implementation of Wearable Technology in Mental Healthcare
19.5.2 Lessons Learned and Best Practices
19.6 Future Directions and Challenges
19.6.1 Emerging Trends in Wearable Technology for Mental Health
19.6.2 Challenges and Opportunities for Further Development
19.7 Conclusion
References
20. Ethical Considerations in Mental Health Data Collection and Analysis: Ethical and Legal Aspects of Mental Health Data in Wearable Technology
V. M. Thorat and M. P. Ambali
20.1 Introduction
20.1.1 Overview of Wearable Technology in Mental Health
20.1.2 Importance of Ethical and Legal Considerations in Data Collection and Analysis
20.2 Ethical Principles in Mental Health Data Collection and Analysis
20.2.1 Autonomy
20.2.2 Beneficence
20.2.3 Non-Maleficence
20.2.4 Justice
20.3 Legal Frameworks for Mental Health Data in Wearable Technology
20.4 Informed Consent in Wearable Technology
20.4.1 Challenges in Obtaining Informed Consent
20.4.2 Strategies for Ensuring Informed Consent
20.5 Data Security and Privacy in Mental Health Wearables
20.5.1 Security Risks in Data Collection and Transmission
20.5.2 Privacy Protection Measures
20.6 Potential Misuse of Mental Health Data
20.6.1 Stigmatization and Discrimination
20.6.2 Commercialization of Mental Health Data
20.7 Ensuring Equity in Access to Mental Health Wearables
20.7.1 Addressing Technological Barriers
20.7.1.1 Addressing Financial Barriers
20.7.2 Promoting Inclusivity in Wearable Technology Design
20.8 Ethical Guidelines for Researchers and Practitioners
20.8.1 Best Practices for Data Collection and Analysis
20.8.2 Ethical Decision-Making Frameworks
20.9 Conclusion
References
21. Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment
Sujata Raghunath Kanetkar and V. M. Thorat
21.1 Introduction
21.1.1 Overview of AI in Healthcare
21.1.2 Importance of Ethical Considerations in AI Deployment
21.2 Ethical Principles in AI Deployment
21.2.1 Principles of Beneficence, Non-Maleficence, Autonomy, and Justice
21.2.2 Transparency and Explainability in AI Algorithms
21.2.3 Accountability and Responsibility in AI Decision-Making
21.3 Challenges in AI Deployment in Urology and Gastroenterology
21.3.1 Data Privacy and Security Concerns
21.3.2 Bias and Fairness in AI Algorithms
21.3.3 Clinical Integration and Acceptance of AI Technologies
21.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology
21.4.1 Data Governance and Management
21.4.2 Patient Consent and Privacy Protection
21.4.3 Bias Mitigation Strategies in AI Algorithms
21.4.4 Clinical Validation and Evaluation of AI Technologies
21.5 Case Studies
21.5.1 Use of AI in Urology for Early Detection of Prostate Cancer
21.5.2 Use of AI in Gastroenterology for Diagnosing Digestive Disorders
21.6 Future Directions and Recommendations
21.6.1 Advancements in AI Ethics and Regulation
21.6.2 Collaboration Between Stakeholders for Ethical AI Deployment
21.6.3 Continuous Monitoring and Evaluation of AI Technologies
21.7 Conclusion
References
22. Future Directions and Opportunities in AI-Driven Healthcare: Family Medicine and Anesthesiological Future Directions in AI-Driven Healthcare
Vithal K. Dhulkhed and Shekhar M. Kumbhar
22.1 Introduction
22.1.1 Overview of AI in Healthcare
22.1.2 Scope of the Chapter
22.2 AI Applications in Family Medicine
22.2.1 Electronic Health Record (EHR) Management
22.2.2 Patient Triage and Risk Stratification
22.2.3 Chronic Disease Management
22.2.4 Telemedicine and Remote Monitoring
22.2.5 Patient Engagement and Education
22.3 AI Applications in Anesthesiology
22.3.1 Preoperative Assessment and Planning
22.3.2 Monitoring During Surgery and Helping with Decisions
22.3.3 Postoperative Care and Recovery
22.3.4 Anesthesia Drug Dosing and Management
22.4 Current Challenges and Limitations
22.4.1 Data Quality and Accessibility
22.4.2 Legal and Ethical Considerations
22.4.3 Integration with Existing Healthcare Systems
22.4.4 Patient Privacy and Consent
22.5 Future Directions and Opportunities
22.5.1 Advancements in Machine Learning and Deep Learning
22.5.2 Enhanced Clinical Decision Support Systems
22.5.3 Personalized Medicine and Treatment Planning
22.5.4 AI-Driven Research and Drug Discovery
22.6 Case Studies and Examples
22.6.1 Successful AI Implementations in Family Medicine
22.6.2 New Ways That AI is Being Used in Anesthesiology
22.7 Conclusion
References
Index
Back to Top
Preface
1. Omics Data Integration in AI System for Immediate and Carryover Effects of Neurodynamic Exercises on SLR Ranges Among Acute PIVD Patients
Durga Bahuguna, Vaibhav Agarwal and Manish Kumar Jha
1.1 Introduction
1.2 Literature Review
1.3 Methodology
1.3.1 Research Question
1.3.2 Aim of the Study
1.3.3 Need of the Study
1.4 Hypothesis
1.4.1 Alternate Hypothesis
1.4.2 Null Hypothesis
1.4.3 Study Design
1.4.4 Study Center
1.4.5 Study Population
1.4.6 Sampling
1.4.7 Study Duration
1.4.8 Sample Size
1.4.9 Inclusion Criteria
1.4.10 Exclusion Criteria
1.4.11 Outcome Variables
1.4.12 Instrumentation
1.5 Procedure
1.6 Intervention
1.6.1 Four Levels of Neurodynamics
1.6.2 Control Group Intervention
1.7 Data Analysis
1.8 Result
1.9 Discussion
1.10 Conclusion
References
2. Effectiveness of Graph-Based Methods for Biological Networks for Primal Reflex Release Techniques on Pain and Disability in Cervicogenic Headache Patient
Siddhartha Kuriya, Manish Kumar Jha, Vaibhav Agarwal and Amit Sharma
2.1 Introduction
2.2 Literature Review
2.3 Methodology
2.3.1 Need of the Study
2.3.2 Aim of the Study
2.3.3 Hypothesis
2.3.3.1 Experimental Hypothesis
2.3.3.2 Null Hypothesis
2.3.4 Protocol
2.4.1 Procedure
2.4.2 OMNE
2.4.3 Clinical Relevance
2.4.4 Limitation of the Study
2.4.5 Scope of Future Research
2.5 Conclusion
References
3. Application of AI in Determining Immediate and Carryover Effects of Primal Reflex Release Technique Neural Reboot on SI Joint Mobility
Rakesh Chaudhary, Vaibhav Agarwal and Aashish Negi
3.1 Introduction
3.2 Literature Review
3.3 Mehodology
3.3.1 Aim of Study
3.3.2 Need of the Study
3.3.3 Hypothesis
3.3.3.1 Null Hypothesis
3.3.3.2 Alternate Hypothesis
3.3.4 Operational Definition
3.3.4.1 Desk Job Worker
3.3.5 Data Analysis
3.3.6 Protocol
3.3.7 Procedure
3.4 Results
3.4.1 Gender and Age Frequency
3.4.2 Experimental and Control Group Scores
3.4.3 Clinical Implication
3.4.4 Future Research
3.4.5 Limitation
3.5 Conclusion
References
4. Future Trends in Bioinformatics AI Integration for Analyzing Immediate Effect of Primal Reflex Release Technique in Neck Pain and Stiffness Patients
Aditya Sharma, Manish Kumar Jha, Vaibhav Agarwal and Amit Sharma
4.1 Introduction
4.2 Literature Review
4.3 Methodology
4.3.1 Need of Study
4.3.2 Aim of the Study
4.3.3 Experimental Hypothesis
4.3.3.1 Null Hypothesis
4.3.3.2 Instrumentation
4.3.4 Inclusion Criteria
4.3.5 Exclusion Criteria
4.3.6 Protocol
4.3.7 Procedure
4.3.8 Statistical Analysis
4.3.9 Distribution of Study Subjects
4.4 Conclusion
References
5. Evolutionary Computation in Bioinformatics Analyzing the Effects of Neurodynamic Exercises on Pain and Disability in Carpal Tunnel Syndrome Patients
Manish Pathak, Vaibhav Agarwal and Manish Kumar Jha
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.3.1 Objectives
5.3.2 Hypothesis
5.3.2.1 Null Hypothesis
5.3.2.2 Experimental Hypothesis
5.3.3 Aim of Study
5.3.3.1 Study Design
5.3.3.2 Study Center
5.3.3.3 Study Population
5.3.3.4 Sample Method
5.3.3.5 Sample Size
5.3.4 Procedure
5.3.4.1 Protocol
5.3.5 Experimental Group Intervention
5.3.5.1 Exercises
5.3.5.2 Exercise Methods
5.3.6 Data Analysis
5.4 Result and Discussion
5.5 Conclusion
References
6. Imperative Role of Artificial Intelligence and Nanotechnology in Healthcare Sector for Sustainable Development
Sheela Bijlwan
6.1 Introduction
6.2 Literature Review
6.3 AI Applications in Healthcare Industries
6.4 Application of AI and Nanotechnology in Medicine
6.5 AI and Nanotechnology in Anti-Aging Medicines
6.6 Result
6.7 Discussion
6.8 Conclusion
References
7. Holistic Approaches for Sleep Pattern Enhancement Using AI and Yoga Therapy: A Comprehensive Scientific Approach
Somlata Jha and Siddhant Rajhans
7.1 Introduction
7.2 Understanding Sleep Patterns
7.3 The Importance of Quality Sleep
7.4 The Role of AI in Sleep Pattern Enhancement
7.5 How AI Works to Enhance Sleep
7.6 How Yoga Influences Sleep
7.7 Combining AI and Yoga for Better Sleep
7.8 The Synergy of AI and Yoga
7.9 Scientific Research on Sleep Improvement
7.10 The Mind–Body Connection
7.11 A Holistic Approach to Sleep
7.12 Practicing Yoga for Better Sleep
7.13 AI-Enabled Sleep Tracking Devices
7.14 Personalized Sleep Plans
7.15 Lifestyle Factors and Sleep
7.16 Conclusion
References
8. Ethical Consideration in Bioinformatics in AI for Analyzing the Effects of Using SHAT Device on Upper Extremity Functions in Stroke Patients
Kapil Lakhwara, Vaibhav Agarwal and Manish Kumar Jha
8.1 Introduction
8.2 SHAT Device (Synchronized Hand Arm Training Device)
8.3 How to Perform SHAT Exercises
8.4 Literature Review
8.5 Methodology
8.5.1 Need of the Study
8.5.2 Aim of the Study
8.5.3 Experimental Hypothesis
8.5.3.1 Null Hypothesis
8.5.4 Study Design
8.5.4.1 Inclusion Criteria
8.5.4.2 Exclusion Criteria
8.6 Protocol
8.7 Procedure
8.8 Results
8.8.1 Gender and Age Frequency
8.8.2 Control Group Scores
8.8.3 Experimental Group Scores
8.8.4 Comparison of Means
8.9 Discussion
8.9.1 Clinical Implication
8.9.2 Future Research
8.9.3 Limitation
8.10 Conclusion
References
9. AI-Driven Drug Discovery and Repurposing for Analyzing Long-Term Effects of Nerve Sliders and Tensioners on Quality of Life in Cervicogenic Headache Patients
Shashwat Pandya, Manish Kumar Jha, Vaibhav Agarwal and Amit Sharma
9.1 Introduction
9.2 Literature Review
9.3 Methodology
9.3.1 Aim of the Study
9.3.2 Need of the Study
9.3.3 Hypothesis
9.3.3.1 Experimental Hypothesis
9.3.3.2 Null Hypothesis
9.4 Protocol
9.5 Procedure
9.6 Discussion
9.7 Conclusion
References
10. Using Artificial Intelligence (AI) Analyzing Recent Advancements in the Anti-Cancerous Properties of Edible Mushrooms and Their Association with the Mode of Action of Polysaccharides
Vishal Rajput, Manish Tenguria, Sanjay Gupta, Neha Sharma and Smriti Rai
10.1 Introduction
10.1.1 Composition of Polysaccharides Derived from Ganoderma
10.2 Polysaccharide Metabolism and Bioavailability
10.2.1 Preventive Measures of Cancer Cell Migration and Proliferation
10.2.2 Pathways Controlling Cell Death
10.2.2.1 Augmentation of Therapeutic Sensitivity
10.3 Conclusion
References
11. Impact of Artificial Intelligence (AI) in Bioremediation of Dairy Effluent by Microalgae and the Potential Application of the Produced Lipid Byproducts
Nisha Dhillon, Vivek Kumar, Geeta Bhandari and Sanjay Gupta
11.1 Introduction
11.1.1 Type of Wastes While Dairy Processing
11.1.2 Technologies Used for the Treatment of Dairy Effluent
11.2 Microalgae
11.3 Lipid-Producing Microalgal Strains
11.4 Biosynthesis of Lipid in Microalgae
11.5 Applications of Microalgal Lipids
11.5.1 In Food and Nutrition
11.5.2 In the Field of Pharmaceuticals
11.5.3 In the Field of Biofuel
11.5.3.1 As Biodiesel
11.5.3.2 As Biohydrogen
11.6 Challenges in the Field of Microalgal Biomass Productivity
11.7 Conclusion
References
12. Smart Collision Recognition and Reporting System with GPS and GSM Integration
Gaurav Aggarwal, Pooja Joshi and Ashutosh Bhatt
12.1 Introduction
12.2 Literature Review
12.3 Block Diagram
12.4 Methodology
12.4.1 Arduino Uno
12.4.2 GSM Module
12.4.3 GPS Module
12.4.4 Shock Sensor
12.4.5 Temperature Sensor
12.4.6 Automobile Collision Reporting System
12.5 Conclusion
References
13. Evolution and Impact of Wearable Devices in Healthcare: Anatomy of Wearable Technology and its Influence on Medical Sciences
M. P. Ambali and V. C. Patil
13.1 Introduction
13.1.1 Wearable Technology in Healthcare
13.1.2 Purpose and Objectives of the Chapter
13.2 Historical Development of Wearable Technology
13.2.1 Early Wearable Devices in Healthcare
13.2.2 Technological Advancements Leading to Modern Wearable Devices
13.3 Anatomy of Wearable Technology
13.3.1 Sensor Technologies in Wearable Devices
13.3.2 Connectivity and Data Transmission
13.3.3 Power Management and Battery Life
13.3.4 Design and Ergonomics
13.4 Types of Wearable Devices in Healthcare
13.4.1 Trackers for Fitness
13.4.2 Wearable Technology
13.4.3 Devices for Medical Monitoring
13.4.4 Wearable Drug Delivery Systems
13.5 Applications of Wearable Technology in Medical Sciences
13.5.1 Remote Patient Monitoring
13.5.2 Chronic Disease Management
13.5.3 Rehabilitation and Physical Therapy
13.5.4 Health and Wellness Promotion
13.6 Impact of Wearable Technology on Healthcare
13.6.1 Improved Patient Outcomes
13.6.2 Enhanced Doctor–Patient Communication
13.6.3 Cost-Effectiveness and Efficiency in Healthcare Delivery
13.6.4 Challenges and Limitations
13.7 Future Trends and Innovations in Wearable Technology
13.7.1 Artificial Intelligence and Machine Learning in Wearable Devices
13.7.2 Integrating Wearable Devices With Electronic Health Records
13.7.3 Ethical and Legal Considerations in Wearable Technology
13.8 Conclusion
References
14. Current State of Wearable Healthcare Technology: Physiology and Biochemistry of Wearable Sensors and Devices
Jyotsna A. Patil and Shrirang N. Patil
14.1 Introduction
14.1.1 Overview of Wearable Healthcare Technology
14.1.2 Importance of Monitoring Physiology and Biochemistry
14.2 Physiological Monitoring
14.2.1 Heart Rate Monitoring
14.2.2 Body Temperature Monitoring
14.2.3 Blood Pressure Monitoring
14.2.4 Respiratory Rate Monitoring
14.2.5 Activity and Sleep Monitoring
14.3 Biochemical Monitoring
14.3.1 Glucose Monitoring
14.3.2 Lactate Monitoring
14.3.3 Cortisol Monitoring
14.3.4 Other Biochemical Markers
14.4 Integration of Sensors and Devices
14.4.1 Wearable Sensor Technologies
14.4.2 Integration of Sensors into Wearable Devices
14.4.3 Data Transmission and Connectivity
14.5 Applications in Healthcare
14.5.1 Chronic Disease Management
14.5.2 Monitoring for Fitness and Sports
14.5.3 Stress Monitoring
14.5.4 Remote Patient Monitoring
14.6 Advances in Data Analytics and Artificial Intelligence
14.6.1 Machine Learning and Predictive Analytics
14.6.2 Real-Time Health Monitoring and Alerts
14.6.3 Personalized Healthcare and Treatment Planning
14.6.4 Data Security and Privacy
14.7 Challenges and Future Directions
14.7.1 Data Privacy and Security
14.7.2 Reliability and Accuracy of Wearable Devices
14.7.3 Interoperability with Healthcare Systems
14.7.4 Future Trends in Wearable Healthcare Technology
14.8 Conclusion
References
15. Real-Time Data Acquisition and Analysis Techniques: Microbiological and Immunological Aspects of Real-Time Health Data Collection
Satish R. Patil and Supriya S. Patil
15.1 Introduction
15.1.1 Overview of Real-Time Data Acquisition and Analysis Techniques
15.1.2 Importance of Microbiological and Immunological Data in Healthcare
15.2 Real-Time Microbiological Data Collection
15.2.1 Monitoring Microbial Populations in Healthcare Settings
15.2.2 Detection of Infectious Diseases and Antimicrobial Resistance
15.2.3 Applications in Infection Control and Outbreak Prevention
15.3 Real-Time Immunological Data Collection
15.3.1 Monitoring Immune Response to Pathogens and Vaccines
15.3.2 Use in Tracking Disease Progression and Predicting Immune-Related Disorders
15.3.3 Clinical Applications in Personalized Medicine and Patient Care
15.4 Technology and Tools for Real-Time Data Collection
15.4.1 Sensors and Devices for Microbiological and Immunological Data
15.4.2 Data Acquisition and Transmission Protocols
15.4.3 Integration with Existing Healthcare Systems
15.5 Data Analysis Techniques
15.5.1 Statistical Analysis of Real-Time Microbiological and Immunological Data
15.5.2 Machine Learning and AI Approaches for Pattern Recognition and Prediction
15.5.3 Visualization Techniques for Interpreting Complex Data
15.6 Case Studies and Applications
15.6.1 Real-World Examples of Real-Time Data Collection and Analysis in Healthcare
15.6.2 Impact on Patient Outcomes and Healthcare Efficiency
15.7 Future Directions and Challenges
15.7.1 Emerging Technologies and Trends in Real-Time Data Collection
15.7.2 Ethical and Regulatory Considerations
15.7.3 Challenges in Implementation and Adoption
15.8 Conclusion
References
16. Applications of Real-Time Data in Healthcare Interventions: Pharmacological Interventions Based on Real-Time Data Analytics
V. M. Thorat and Satish V. Kakade
16.1 Introduction
16.1.1 Overview of Real-Time Data Analytics in Healthcare
16.1.1.1 Real-Time Monitoring and Alerts
16.1.1.2 Customized Healthcare
16.1.1.3 Analytics for Prediction
16.1.1.4 Managing the Health of a Population
16.1.2 Importance of Real-Time Data in Pharmacological Interventions
16.2 Personalized Medicine
16.2.1 Role of Real-Time Data in Personalized Medicine
16.2.2 Examples of Personalized Medicine in Pharmacological Interventions
16.2.3 Benefits and Challenges of Personalized Medicine
16.3 Treatment Efficacy
16.3.1 Enhancing Treatment Efficacy Through Real-Time Data Analytics
16.3.2 Case Studies Demonstrating Improved Treatment Outcomes
16.3.3 Strategies for Optimizing Treatment Efficacy Using Real-Time Data
16.4 Proactive Interventions
16.4.1 Real-Time Monitoring for Proactive Interventions
16.4.2 Examples of Proactive Interventions in Chronic Disease Management
16.4.3 Impact on Patient Outcomes and Healthcare Costs
16.5 Clinical Trials
16.5.1 Real-Time Data Analytics in Clinical Trial Design
16.5.2 Adaptive Trial Designs Enabled by Real-Time Data
16.5.3 Accelerating Drug Development with Real-Time Data Analytics
16.6 Challenges and Future Directions
16.6.1 Ethical Considerations in Real-Time Data Analytics
16.6.2 Addressing Data Privacy and Security Concerns
16.6.3 Future Trends and Innovations in Real-Time Data Analytics for Pharmacological Interventions
16.7 Conclusion
References
17. Strategies for Integrating Wearable Technology with Healthcare Systems: Pathological Considerations in Wearable Device Integration
Sujata Raghunath Kanetkar and Vaishali V. Raje
17.1 Introduction
17.1.1 Overview of Wearable Technology in Healthcare
17.1.2 Importance of Integrating Wearable Devices with Healthcare Systems
17.2 Pathological Considerations in Wearable Device Integration
17.2.1 Challenges in Collecting and Interpreting Pathological Data
17.2.2 Specialized Sensors and Algorithms for Pathological Data
17.2.3 Interoperability with Existing Healthcare Systems
17.3 Strategies for Effective Integration
17.3.1 Developing Standards for Data Exchange
17.3.2 Implementing Robust Security Measures
17.3.3 Ensuring Seamless Integration with Electronic Health Records (EHRs)
17.3.4 Enhancing User Engagement and Compliance
17.4 Case Studies and Examples
17.4.1 Successful Integration of Wearable Technology in Pathological Monitoring
17.4.2 Lessons Learned and Best Practices
17.5 Future Directions and Emerging Trends
17.5.1 Advances in Sensor Technology for Pathological Monitoring
17.5.2 Integration with Artificial Intelligence and Machine Learning
17.5.3 Regulatory Considerations and Policy Implications
17.6 Conclusion
References
18. Challenges and Solutions in Healthcare System Integration: Histological Perspectives on Wearable Device Integration Challenges
Yugantara R. Kadam and Sujata Raghunath Kanetkar
18.1 Introduction
18.1.1 Overview of Wearable Devices in Healthcare
18.1.2 Importance of Integration in Healthcare Systems
18.2 Challenges in Integrating Wearable Devices
18.2.1 Interoperability Issues
18.2.2 Security and Privacy Concerns
18.2.3 Accuracy and Reliability of Data
18.2.4 Scalability and Sustainability
18.3 Interoperability Challenges
18.3.1 Different Data Formats and Standards
18.3.2 Integration with Electronic Health Records (EHRs)
18.4 Security and Privacy Concerns
18.4.1 Data Encryption
18.4.2 Authorization and Access Control
18.4.3 Compliance with Regulations (e.g., HIPAA)
18.5 Accuracy and Reliability of Data
18.5.1 Sensor Technology
18.5.2 Data Calibration
18.5.3 User Compliance and Education
18.6 Scalability and Sustainability
18.6.1 Cloud-Based Solutions
18.6.2 Needs for Infrastructure
18.6.3 Cost Considerations
18.7 Solutions to Integration Challenges
18.7.1 Development and Adoption of Standards
18.7.2 Enhanced Security Measures
18.7.3 Improvements in Sensor Technology
18.7.4 Cloud-Based Storage and Processing Solutions
18.8 Case Studies and Best Practices
18.8.1 Successful Integration Examples
18.8.2 Lessons Learned
18.8.3 Recommendations for Healthcare Systems
18.9 Future Trends and Outlook
18.9.1 Advancements in Wearable Technology
18.9.2 Integration Challenges and Opportunities
18.9.3 Implications for Healthcare Delivery
18.10 Conclusion
References
19. Role of Wearable Technology in Mental Health Monitoring and Management: Psychiatric Insights into Wearable Technology Adoption
Sharad V. Kshirsagar and V. C. Patil
19.1 Introduction
19.1.1 Overview of Wearable Technology in Mental Health Monitoring and Management
19.1.2 Importance of Psychiatric Insights in Wearable Technology Adoption
19.2 Adoption of Wearable Technology in Mental Healthcare
19.2.1 Things that Affect Adoption
19.2.2 Psychiatric Perspectives on User Acceptance
19.2.3 Design Considerations for User-Friendly Wearable Devices
19.3 Data Privacy and Security in Mental Healthcare
19.3.1 Importance of Data Privacy in Mental Health Monitoring
19.3.2 Psychiatric Insights into Secure Data Management Systems
19.3.3 Regulatory Compliance in Wearable Technology Adoption
19.4 Potential Impact of Wearable Technology in Mental Healthcare
19.4.1 Early Detection of Symptoms
19.4.2 Personalized Interventions
19.4.3 Patient Engagement and Self-Management
19.5 Case Studies and Examples
19.5.1 Successful Implementation of Wearable Technology in Mental Healthcare
19.5.2 Lessons Learned and Best Practices
19.6 Future Directions and Challenges
19.6.1 Emerging Trends in Wearable Technology for Mental Health
19.6.2 Challenges and Opportunities for Further Development
19.7 Conclusion
References
20. Ethical Considerations in Mental Health Data Collection and Analysis: Ethical and Legal Aspects of Mental Health Data in Wearable Technology
V. M. Thorat and M. P. Ambali
20.1 Introduction
20.1.1 Overview of Wearable Technology in Mental Health
20.1.2 Importance of Ethical and Legal Considerations in Data Collection and Analysis
20.2 Ethical Principles in Mental Health Data Collection and Analysis
20.2.1 Autonomy
20.2.2 Beneficence
20.2.3 Non-Maleficence
20.2.4 Justice
20.3 Legal Frameworks for Mental Health Data in Wearable Technology
20.4 Informed Consent in Wearable Technology
20.4.1 Challenges in Obtaining Informed Consent
20.4.2 Strategies for Ensuring Informed Consent
20.5 Data Security and Privacy in Mental Health Wearables
20.5.1 Security Risks in Data Collection and Transmission
20.5.2 Privacy Protection Measures
20.6 Potential Misuse of Mental Health Data
20.6.1 Stigmatization and Discrimination
20.6.2 Commercialization of Mental Health Data
20.7 Ensuring Equity in Access to Mental Health Wearables
20.7.1 Addressing Technological Barriers
20.7.1.1 Addressing Financial Barriers
20.7.2 Promoting Inclusivity in Wearable Technology Design
20.8 Ethical Guidelines for Researchers and Practitioners
20.8.1 Best Practices for Data Collection and Analysis
20.8.2 Ethical Decision-Making Frameworks
20.9 Conclusion
References
21. Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment
Sujata Raghunath Kanetkar and V. M. Thorat
21.1 Introduction
21.1.1 Overview of AI in Healthcare
21.1.2 Importance of Ethical Considerations in AI Deployment
21.2 Ethical Principles in AI Deployment
21.2.1 Principles of Beneficence, Non-Maleficence, Autonomy, and Justice
21.2.2 Transparency and Explainability in AI Algorithms
21.2.3 Accountability and Responsibility in AI Decision-Making
21.3 Challenges in AI Deployment in Urology and Gastroenterology
21.3.1 Data Privacy and Security Concerns
21.3.2 Bias and Fairness in AI Algorithms
21.3.3 Clinical Integration and Acceptance of AI Technologies
21.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology
21.4.1 Data Governance and Management
21.4.2 Patient Consent and Privacy Protection
21.4.3 Bias Mitigation Strategies in AI Algorithms
21.4.4 Clinical Validation and Evaluation of AI Technologies
21.5 Case Studies
21.5.1 Use of AI in Urology for Early Detection of Prostate Cancer
21.5.2 Use of AI in Gastroenterology for Diagnosing Digestive Disorders
21.6 Future Directions and Recommendations
21.6.1 Advancements in AI Ethics and Regulation
21.6.2 Collaboration Between Stakeholders for Ethical AI Deployment
21.6.3 Continuous Monitoring and Evaluation of AI Technologies
21.7 Conclusion
References
22. Future Directions and Opportunities in AI-Driven Healthcare: Family Medicine and Anesthesiological Future Directions in AI-Driven Healthcare
Vithal K. Dhulkhed and Shekhar M. Kumbhar
22.1 Introduction
22.1.1 Overview of AI in Healthcare
22.1.2 Scope of the Chapter
22.2 AI Applications in Family Medicine
22.2.1 Electronic Health Record (EHR) Management
22.2.2 Patient Triage and Risk Stratification
22.2.3 Chronic Disease Management
22.2.4 Telemedicine and Remote Monitoring
22.2.5 Patient Engagement and Education
22.3 AI Applications in Anesthesiology
22.3.1 Preoperative Assessment and Planning
22.3.2 Monitoring During Surgery and Helping with Decisions
22.3.3 Postoperative Care and Recovery
22.3.4 Anesthesia Drug Dosing and Management
22.4 Current Challenges and Limitations
22.4.1 Data Quality and Accessibility
22.4.2 Legal and Ethical Considerations
22.4.3 Integration with Existing Healthcare Systems
22.4.4 Patient Privacy and Consent
22.5 Future Directions and Opportunities
22.5.1 Advancements in Machine Learning and Deep Learning
22.5.2 Enhanced Clinical Decision Support Systems
22.5.3 Personalized Medicine and Treatment Planning
22.5.4 AI-Driven Research and Drug Discovery
22.6 Case Studies and Examples
22.6.1 Successful AI Implementations in Family Medicine
22.6.2 New Ways That AI is Being Used in Anesthesiology
22.7 Conclusion
References
Index
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