Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.
The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Preface - p. xv 8. Acknowledgment - p. xvii 9. Chapter 1. The digital health revolution: natural language processing technologies reshaping patient care and medical documentation - p. 1 10. Chapter 2. Large language models and generative AI in healthcare: Multimodal intelligence, clinical integration, and the future of medical practice - p. 31 11. Chapter 3. Navigating the utility of generative artificial intelligence in healthcare delivery - p. 59 12. Chapter 4. Generative artificial intelligence in medicine - p. 79 13. Chapter 5. Advancing patient care with conversational AI: Applications, challenges, and future directions - p. 109 14. Chapter 6. The voice revolution in medicine: Reshaping clinical workflows with voice assistants and speech recognition - p. 131 15. Chapter 7. Machines that understand illness: Natural language processing–based hospital kiosk systems - p. 157 16. Chapter 8. Telehealth workspaces for healthcare providers - p. 179 17. Chapter 9. AI-driven innovations in infectious disease detection and control - p. 203 18. Chapter 10. Depression identification from social media using n-gram based deep neural network - p. 227 19. Chapter 11. Healytix: Comparative analysis of classification algorithms and deep learning optimizers for cardiac disease detection - p. 253 20. Chapter 12. 3D U-Net based segmentation of liver vessels from computed tomography images - p. 281 21. Chapter 13. Revolutionizing patient care with digital twins: A smart healthcare perspective - p. 293 22. Chapter 14. Legal and regulatory compliance in digital twin-enabled health care - p. 313 23. Chapter 15. Multilingual NLP, personalization, and global health - p. 331 24. Chapter 16. AI for multilingual, human-centered personalization, and public health - p. 353 25. Chapter 17. Data privacy, security, and ethics in medical NLP - p. 373 26. Chapter 18. Federated learning, explainability, and the road ahead - p. 393 27. Index - p. 413
Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.
The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xi 7. Preface - p. xv 8. Acknowledgment - p. xvii 9. Chapter 1. The digital health revolution: natural language processing technologies reshaping patient care and medical documentation - p. 1 10. Chapter 2. Large language models and generative AI in healthcare: Multimodal intelligence, clinical integration, and the future of medical practice - p. 31 11. Chapter 3. Navigating the utility of generative artificial intelligence in healthcare delivery - p. 59 12. Chapter 4. Generative artificial intelligence in medicine - p. 79 13. Chapter 5. Advancing patient care with conversational AI: Applications, challenges, and future directions - p. 109 14. Chapter 6. The voice revolution in medicine: Reshaping clinical workflows with voice assistants and speech recognition - p. 131 15. Chapter 7. Machines that understand illness: Natural language processing–based hospital kiosk systems - p. 157 16. Chapter 8. Telehealth workspaces for healthcare providers - p. 179 17. Chapter 9. AI-driven innovations in infectious disease detection and control - p. 203 18. Chapter 10. Depression identification from social media using n-gram based deep neural network - p. 227 19. Chapter 11. Healytix: Comparative analysis of classification algorithms and deep learning optimizers for cardiac disease detection - p. 253 20. Chapter 12. 3D U-Net based segmentation of liver vessels from computed tomography images - p. 281 21. Chapter 13. Revolutionizing patient care with digital twins: A smart healthcare perspective - p. 293 22. Chapter 14. Legal and regulatory compliance in digital twin-enabled health care - p. 313 23. Chapter 15. Multilingual NLP, personalization, and global health - p. 331 24. Chapter 16. AI for multilingual, human-centered personalization, and public health - p. 353 25. Chapter 17. Data privacy, security, and ethics in medical NLP - p. 373 26. Chapter 18. Federated learning, explainability, and the road ahead - p. 393 27. Index - p. 413
