Chapter 1. Methods for the recognition of multisource data in intelligent medicine: A review and next generation trends -- Chapter 2. Deep Learning in Healthcare: Challenges and Opportunities -- Chapter 3. Examination of Health Data Performance Depending on the Creative Use of Optimization Methods and Machine Learning Algorithms -- Chapter 4. Effect of computation and cognitive bias in healthcare intelligence and pharmacogenomics -- Chapter 5. Application of Genetic Algorithms in Healthcare: A Review -- Chapter 6. Decision-Making in Healthcare Nano-informatics -- Chapter 7. A Succinct Analytical Study of the Usability of Encryption Methods in Healthcare Data Security -- Chapter 8. IoMT in healthcare industry - Concepts and Applications -- Chapter 9. The Effect of Heuristic Methods Towards Performance of Health Data Analysis -- Chapter 10. AI for Stress Diagnosis at Home Environment -- Chapter 11. Contemporary Technologies to Combat Pandemics and Epidemics -- Chapter 12. Deep Learning for Diabetic Retinopathy Detection: Challenges and Opportunities -- Chapter 13. Deep Reinforcement Based Conversational AI Agent in Healthcare System -- Chapter 14. Deep Learning Empowered Fight against COVID-19: A Survey -- Chapter 15. Application of GAN in Guided Imagery Therapy -- Chapter 16. Digital Transformation in Healthcare Industry: A Survey -- Chapter 17. Application of Deep Learning in Mental Disorder: Challenges and Opportunities.

This edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.