Handbook on Augmenting Telehealth Services: Using Artificial Intelligence provides knowledge of AI-empowered telehealth systems for efficient healthcare services. The handbook discusses novel innovations in telehealth using AI techniques and also focuses on emerging tools and techniques in smart health systems. The book highlights important topics such as remote diagnosis of patients and presents e-health data management showcasing smart methods that can be used to improvise healthcare support and services. The handbook also shines a light on future trends in AI-enabled telehealth systems.
Features
Provides knowledge of AI-empowered telehealth systems for efficient healthcare services
Discusses novel innovations in telehealth using AI techniques
Covers emerging tools and techniques in smart health systems
Highlights remote diagnosis of patients
Focuses on e-health data management and showcases smart methods used to improvise healthcare support and services
Shines a light on future trends in AI-enabled telehealth systems
Every individual (patients, doctors, healthcare staff, etc.) is currently getting adapted to this new evolution of healthcare. This handbook is a must-read for students, researchers, academicians, and industry professionals working in the field of artificial intelligence and its uses in the healthcare sector.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Professional and Professional Reference
Illustrationen
77 s/w Abbildungen, 14 s/w Photographien bzw. Rasterbilder, 63 s/w Zeichnungen, 17 s/w Tabellen
17 Tables, black and white; 63 Line drawings, black and white; 14 Halftones, black and white; 77 Illustrations, black and white
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 26 mm
Gewicht
ISBN-13
978-1-032-38546-4 (9781032385464)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Dr. Sonali Vyas has served as an academician and researcher for over 13 years. Currently, she is working as an Associate Professor at the University of Petroleum and Energy Studies, Uttarakhand. She is a professional member of Senior Member-IEEE, ACM-India, CSI, IFERP, IAENG, ISOC, SCRS and IJERT. She has been awarded the "National Distinguished Educator Award 2021", by the International Institute of organized Research (I2OR) which is a registered MSME, Government of India". She was also awarded the "Best Academician of the Year Award (Female)" in "Global Education and Corporate Leadership (GECL-2018)". Dr. Vyas has authored and edited many books with renowned publishers. She has published many research papers, articles, and chapters in refereed journals, conference proceedings, and patents. She has also been a member of the Organizing Committee, National Advisory Board, and Technical Program Committee for many international and national conferences, as well as chaired sessions. Her research interests include Healthcare Informatics, Blockchain, Database Virtualization, Data Mining, and Big Data Analytics.
Dr. Sunil Gupta has over more than twenty years of experience in teaching, research, and industry in the field of Computer science and Engineering. He is an active member of IEEE Society, Computer Society of India, Member, Computer Science Teacher Association, Life Member, International Association of Engineers, Member, International Association of Computer Science and Information Technology, Member, and Internet Society (ISOC). He has conducted various workshops, conferences, and FDP. He has authored sixty-nine research papers, two textbooks, and has six patents to his credit. He has also acted as a reviewer of International Journals and is a member of the Scientific Committee and Editorial Review Board on Engineering and Physical Sciences for the World Academy of Science, Engineering and Technology.
Dr. Monit Kapoor is an academician and a researcher with a cumulative work experience of twenty-four years and is currently guiding six Ph.D. scholars in various research areas. He has more than 30 journal publications, books and filed four patents/copyrights in the area of IOMT, Blockchain and Health Care. He currently works as a Professor in the Department of CSE at Chitkara University, Punjab, India, and has been voted among the Top 50 tech-savvy academicians in India by Ulektz in the year 2019. Dr. Kapoor closely works with the government of India, the Ministry of Electronics, and IT for proposing Mobile Device Security Standards. He has been in TPC of multiple international conferences and serves as a reviewer for SCI and Scopus indexed journals. He has been session chair in many reputed international conferences and has delivered many invited talks and workshops. His research interests are in the area of Machine Learning, Adhoc and Sensor Networks, UAV Networks, Speech Processing, and Artificial Intelligence.
Dr Samiya Khan is an alumna of University of Delhi, India and did her PhD in Computer Science from Jamia Millia Islamia, India. She is currently working as Lecturer in Computer Science at University of Greenwich, United Kingdom. Previously, she served as a postdoctoral research fellow at University of Wolverhampton, United Kingdom. She has contributed more than 20 research papers and her publications extend across journal articles and book chapters in high impact publications of international repute. She has also presented her research at reputed international conferences. She has served as Associate Editor, Scientific India Magazine and reviewer for many reputed journals and conferences. Besides this, she has authored a book entitled 'Big Data and Analytics' and co-edited many books including 'Internet of Things (IoT): Concepts and Applications' published by Springer Nature, Switzerland and 'Extended Reality for Healthcare Systems: Recent Advances in Contemporary Research' published by Elsevier. Samiya's expertise spans across data science, Artificial Intelligence, edge computing and the Internet of Things (IoT) with experience in development of heterogeneous systems.
Herausgeber*in
University of Petroleum and Energy Studies, Dehradun
Chitkara University, Punjab, India
University of Wolverhampton, UK
1. Artificial intelligence and Healthcare. 1.1 Introduction. 1.2 Pre-processing. 1.3 Radiology's use of artificial intelligence and overcoming its challenges. 1.4 Artificial intelligence and X-rays in Medical Imaging. 1.5 Modelling and Simulation Techniques for Edge AI in Healthcare. Conclusion and Future Scope. 2. Revolutionizing Healthcare: Impact of Artificial Intelligence in Disease Diagnosis, Treatment and Patient Care. 2.1 Introduction. 2.2 What is Machine Learning, Deep Learning and Natural Language Processing?. 2.3 Timeline For AI Being Used in Healthcare. 2.4 Use of AI in Different Domains of the Healthcare Industry. 2.5 Use of AI Enabled Applications. 2.6 Challenges and Limitations. 2.7 Conclusion. 3. Applications of Healthcare Products Having AI Capability in Disease Diagnosis. 3.1 Introduction. 3.2 Basics of Artificial Intelligence and Machine Learning. 3.3 Clinical Versus AI-Based Disease Diagnosis. 3.4 Deep Learning and disease diagnosis. 3.5 Artificial Intelligence and Radiology. Conclusion. 4. Application of AI for Disease Prediction. 4.1 Introduction. 4. 2 Importance of Disease Prediction. 4.3 Types of AI Algorithms. 4.4 Application of AI in Disease Prediction. 4.5 Dataset. 4.6 Comparison of the AI model with traditional disease prediction methods. 4.7 Conclusion. 5. The Power of AI in Telemedicine: Improving Access and Outcomes. 5.1 Introduction. 5.2 Overview of Telemedicine and AI Technologies. 5.3 AI-Powered Telemedicine Models. 5.4 Case Studies and Real-World Applications. 5.5 Ethical Considerations and Challenges. 5.6 Future Directions and Opportunities. 5.7 Conclusion. 6. AI Ethics and Challenges in Healthcare. 6.1 Introduction. 6.2 AI in medicine. 6.3 Growth factor of AI in health care. 6.4 Ethical issues in AI driven healthcare. 6.5 Legal issues in AI driven healthcare. 6.6 Conclusion. 7. The Future of the Healthcare System: A Meta-Analysis of Remote Patient Monitoring. 7.1 Introduction. 7.2 Android Application. 7.3 How remote patient monitoring works. 7.4 Benefits of remote patient monitoring. 7.5 RPM (Remote Patient Monitoring). 7.6 Controversy. 7.7 Some Organizations That Are Surprising Telemedicine. Conclusion. 8. Artificial Intelligence for Healthcare Delivery System: Future Prospective. 8.1 Introduction. 8.2 Role of sensors in healthcare Sector. 8.3 Role of Software based Mobile Devices in Healthcare Sector. 8.4 Natural Language Processing (NLP). 8.5 Medical imaging technology utilizing AI. 8.6 Role of AI in Cancer Management. 8.7 Remote-controlled Robotic Surgery. 8.8 Precision Medicine. 8.9 Early Sepsis Detection Using Deep Neural Network. 8.10 Impact of AI on Employment in Developed and Developing Nations. 8.11 Dependency of Doctors over Artificial Intelligence in clinical terms. Conclusion. Future Perspectives. 9. Contemporary Practice of Automated Machine Learning For Clinical Repository in Medicinal Field. 9.1 Introduction. 9.2 Automated Machine Learning. 9.3 Automated Machine Learning in Healthcare Industry. 9.4 Challenges and benefits of Working with Clinical Notes. 9.5 Conclusion and Future Scope. 10. Smart innovative medical devices based on Artificial Intelligence. 10.1 Introduction of AI enabled medical devices. 10.2 Development stages of AI-medical devices. 10.3 Regulatory aspects and guideline. 10.4 Merits and Demerits of medical devices. 10.5 Applications. 10.6 Future of AI driven medical devices and Conclusion. 10.7 References. 11. Virtual Consultation: Scope and Application in Healthcare. 11.1 Technology and Telemedicine. 11.2 Future drivers of Telemedicine. 11.3 Narrative literature review regarding VC in developed and developing countries. 11.4 Telemedicine situation in India11.5 SWOT Analysis. 11.6 PESTLE analysis. 11.7 Telemedicine Practice Guidelines. 11.8 Conclusion. 12. Advance and Smart Health Care System: A Case Study Calo - An AI-based health utility mobile application. 12.1 Introduction. 12.2 Literature Review. 12.3 Methodology. 12.4 Implementation. 12.5 Conclusion and Future Prospectus. 13. Remote Patient Monitoring: An Overview of Technologies, Applications, and Challenges. 13.1 Introduction. 13.2 Types of RPM Devices. 13.3 Applications of RPM. 13.4 Challenges of RPM. 13.5 Advancements in RPM. 13.6 Benefits of RPM. 13.7 Future Directions of RPM. Conclusion. 14. Artificial Intelligence (AI) and Augmented Reality (AR): Legal & Ethical Issues in the Telemedicine / Telehealth Sphere. 14.1 Introduction: Background and Driving Forces. 14.2 Applications of AI and AR in Healthcare. 14.3 Ethical Challenges. 14.4 Legal Challenges. 14.5 Recommendation and Conclusion. 15. Telemedicine: Patient monitoring and electronic healthcare Record storage. 15.1 Introduction. 15.2 Impact of fog computing in data analytics. 15.3 Industrial Internet of Things Technology and Facilitators. 15.4 Characteristics of Fog Computing. 15.5 Taxonomy of fog Data Analytics Communication. 15.6 Data Processing architecture in the Fog. 15.7 IoT Middleware Technology. 15.8 Challenges in Fog Computing. 15.9 Conclusion. 16. Gastric Cancer Diagnosis Using Machine Learning Techniques: A Survey. 16.1 Introduction. 16.2 Traditional Methods Versus AI-based Methods for Gastric Cancer Diagnosis. 16.3 Role of Gastric Cancer in ML-based, Knowledge-based, and Medical Decision Support Systems. 16.4 Related Work. 16.5 Literature Review. 16.6 Results and Discussion. 17. Blockchain and Artificial Intelligence in Telemedicine and Remote Patient Monitoring. 17.1 Introduction. 17.2 Related Healthcare Projects using Blockchain and AI. 17.3 Applications of Blockchain and AI in Healthcare. 17.4 Patient Centric Framework using Blockchain and AI in Telemedicine and Remote Patient Monitoring. 17.5 Challenges associated with using blockchain and AI in Telemedicine and RPM. 17.6 Future avenues for integration and collaboration of HealthCare with Blockchain and AI. 17.7 Conclusion. 18. The Prediction of Critical Health Diseases Using Artificial Intelligence with Lung Cancer as a case study. 18.1 Introduction. 18.2 Literature Survey. 18.3 Importance of Predicting Critical Health Diseases. 18.4 Methods of Using AI to Predict Critical Health Diseases. 18.5 Application of AI technique to predict the Lung Cancer: A Case Study. 18.6 Benefits of Using AI to Predict Critical Health Diseases. 18.7 Potential Limitations of AI in Predicting Critical Health Diseases. 18.8 Future Directions of AI in Predicting Critical Health Diseases. 18.9 Conclusion. 19. Revolutionizing Healthcare: The Impact of Augmented and Virtual Reality. 19.1 Introduction. 19.2 Latest Market Update. 19.3 Impact of COVID19 on the healthcare market for augmented reality and virtual reality. 19.4 Healthcare industry and fresh opportunities. 19.5 Future of AR/VR in the Healthcare Sector. 19.6 Virtual Reality Vs Augmented Reality. 19.7 History of VR. 19.8 Beginning of virtual. 19.9 Virtual reality in the 50s & 60s. 19.10 Virtual reality in the 90s & 00s. 19.11 Case Study. 19.12 Conclusion & Result. 20. Augmented and Virtual Reality-Based Interventions for Learning Disabilities: Current Practices and Future Prospects. 20.1 Introduction. 20.2 Background. 20.3 Learning Disabilities: Types, Causes and Management. 20.4 Discussion. 20.5 Conclusion. 21. Employ Metrics in the Data Warehouse's Requirements Model for Hospitals. 21.1 Introduction. 21.2 Related Work. 21.3 Importance of Requirements Engineering (RE). 21.4 Requirements Engineering Approaches. 21.5 AGDI Model based on RE Approach. 21.6 Hospital Requirements Model of DW based on AGDI Model. 21.7 Requirements Completeness Metrics of Hospital Requirement model of DW. 21.8 Lesson Learnt. 21.9 Conclusion and Future Scope. 22. Paving the way for healthcare with AI, ML, and DL: Opportunities, Challenges, and Open Issues. 22.1 Introduction. 22.2 Opportunities in Healthcare with AI, ML, and DL. 22.3 Challenges in Healthcare with AI, ML, and DL. 22.4 Integration of AI, ML, and DL into existing healthcare systems. 22.5 Open Issues in Healthcare with AI, ML, and DL. 22.6 Conclusion
