Natural Language Processing for Healthcare

The Rise of Intelligent Assistants
Erscheinungsjahr 2026
ISBN: 978-0-443-45252-9
Verlag: Elsevier Science

Buch, Englisch, 444 Seiten, Format (B × H): 190 mm x 236 mm, Gewicht: 1139 g

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.

Autoren/Hrsg.

Shaw, Laxmi

Mahajan, Shubham

Upreti, Kamal

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie

Inhaltsverzeichnis

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

Über Autor(innen)

Upreti, Kamal
Dr. Kamal Upreti is an Associate Professor of Computer Science at CHRIST (Deemed to be University), Ghaziabad. He holds, a Ph.D. in Computer Science & Engineering, and a postdoctoral fellowship at National Taipei University of Business, Taiwan, funded by MHRD.

With teaching, research, and industry exposure, he has produced numerous patents and publications. His interests span modern physics, data analytics, cybersecurity, ML, healthcare, embedded systems, and cloud computing. Notable projects include Hydrastore in Japan, IPDS in India, and an ICMR-funded cardiovascular-prediction project with GB Pant and AIIMS Delhi.

Dr. Upreti serves as session chair, keynote speaker, trainer, and faculty developer, and has been honored as Best Teacher, Best Researcher, and an M.Tech Gold Medalist.

Mahajan, Shubham
Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

Shaw, Laxmi
Dr. Laxmi Shaw is a Researcher and Faculty at Texas A&M University–Victoria, United States, where her work centres on adversarial machine learning, large language models (LLMs), healthcare analytics, fraud detection, and energy management systems. She was previously a Postdoctoral Scholar at Texas State University and a Senior Postdoctoral Fellow (Volunteer) at the University of Texas at Austin. She has worked on projects with Samsung Research & Development and Carrier Corporation (UTC-HRDC). With over a decade of combined research and industry experience, Dr. Shaw has co-authored 5 books and published more than 40 peer-reviewed papers in journals, international conferences, and edited volumes. Her research spans AI/ML security, EEG signal processing, IoT-enabled anomaly detection, Siamese networks, adversarial robustness in LLMs, and GPU-accelerated healthcare analytics. She is a Senior Member of IEEE and an active reviewer for several journals.She earned her Ph.D. in Electrical Engineering with a specialization in Artificial Intelligence and Machine Learning from the prestigious Indian Institute of Technology (IIT) Kharagpur, India. She also holds a Master of Technology (M.Tech) in Instrumentation and Electronics Engineering from Jadavpur University, and a Bachelor of Engineering (B.E.) in Electronics and Instrumentation Engineering from Sambalpur University, Odisha. She has authored three books and over 35 peer-reviewed papers on AI/ML security, EEG processing, IoT anomaly detection, and GPU-accelerated healthcare analytics. A Senior IEEE member and award-winning researcher, she actively reviews for leading journals and is committed to ethical, explainable, and secure AI, especially in healthcare and adversarial contexts.