Full Description
Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical application in healthcare, offering an accessible yet comprehensive 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 crucial concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models including BioBERT and ClinicalBERT, and the emerging impact 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. Healthcare professionals and clinicians will find practical insights into streamlining patient care and diagnostics. Biomedical researchers and data scientists can deepen their understanding of NLP methods tailored to medical data. Students, educators, technology developers, and healthcare administrators alike will benefit from the book's balanced coverage of theory, implementation, and regulation, empowering them to innovate and responsibly deploy intelligent assistants that enhance healthcare delivery worldwide.
Contents
Section I: Foundations of NLP in Healthcare
1. The Digital Health Revolution: Natural Language Processing Technologies Reshaping Patient Care and Medical Documentation
2. Large Language Models and Generative AI in Healthcare: Multimodal Intelligence, Clinical Integration, and the Future of Medical Practice
3. Navigating the Utility of Generative Artificial Intelligence in Healthcare Delivery
4. GENERATIVE ARTIFICIAL INTELLIGENCE IN MEDICINE
Section II: Core Technologies and Approaches
5. Advancing Patient Care with Conversational AI: Applications, Challenges, and Future Directions
6. The Voice Revolution in Medicine: Reshaping Clinical Workflows with Voice Assistants and Speech Recognition
7. MACHINES THAT UNDERSTAND ILLNESS: Natural Language Processing based hospital kiosk systems
8. Telehealth Workspaces for Healthcare Providers
Section III: Applications and Case Studies
9. AI-Driven Innovations in Infectious Disease Detection and Control
10. Depression Identification from Social Media using n-gram based Deep Neural Network
11. HeaLytix: Comparative Analysis of Classification Algorithms and Deep Learning Optimizers For Cardiac Disease Detection
12. 3D U-Net based Segmentation of Liver Vessels from Computed Tomography Images
13. Revolutionizing Patient Care with Digital Twins: A Smart Healthcare Perspective
Section IV: Global, Ethical, and Technical Challenges
14. Legal And Regulatory Compliance In Digital Twin - Enabled Healthcare
15. Multilingual NLP, Personalisation, and Global Health
16. AI for Multilingual, Human Centered Personalization, and Public Health
17. Data Privacy, Security, and Ethics in Medical NLP
18. Federated Learning, Explainability, and the Road Ahead
