Natural Language Processing for Healthcare

The Rise of Intelligent Assistants

Laxmi Shaw, Shubham Mahajan, Kamal Upreti

• Publisher: Academic Press
• Year: 2026
• Topic: Biological Sciences
• eBook ISBN:
  9780443452536
• Subtopic: Biotechnology
• Index: Biological Sciences

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. - Bridges AI research and healthcare practice with accessible, healthcare-focused NLP insights for clinical and operational use - Provides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagement - Addresses ethical, legal, and interoperability challenges unique to healthcare NLP applications - Explores cutting-edge technologies, including large language models and federated learning in real-world medical contexts - Equips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making

Table of contents

• Title of Book
• Chapter 1. The digital health revolution: natural language processing technologies reshaping patient care and medical documentation
• Chapter 2. Large language models and generative AI in healthcare: Multimodal intelligence, clinical integration, and the future of medical practice
• Chapter 3. Navigating the utility of generative artificial intelligence in healthcare delivery
• Chapter 4. Generative artificial intelligence in medicine
• Chapter 5. Advancing patient care with conversational AI: Applications, challenges, and future directions
• Chapter 6. The voice revolution in medicine: Reshaping clinical workflows with voice assistants and speech recognition
• Chapter 7. Machines that understand illness: Natural language processing–based hospital kiosk systems
• Chapter 8. Telehealth workspaces for healthcare providers
• Chapter 9. AI-driven innovations in infectious disease detection and control
• Chapter 10. Depression identification from social media using n-gram based deep neural network
• Chapter 11. Healytix: Comparative analysis of classification algorithms and deep learning optimizers for cardiac disease detection
• Chapter 12. 3D U-Net based segmentation of liver vessels from computed tomography images
• Chapter 13. Revolutionizing patient care with digital twins: A smart healthcare perspective
• Chapter 14. Legal and regulatory compliance in digital twin-enabled health care
• Chapter 15. Multilingual NLP, personalization, and global health
• Chapter 16. AI for multilingual, human-centered personalization, and public health
• Chapter 17. Data privacy, security, and ethics in medical NLP
• Chapter 18. Federated learning, explainability, and the road ahead
• Index