Handbook on Augmenting Telehealth Services

This handbook offers a cross-disciplinary perspective, models, and innovations in telehealth systems that utilize AI technologies such as Machine Learning, Augmented Reality, Virtual Reality, Big Data Management, and IoT as it discusses various methods for remote care support and services.

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