Natural Language Processing for Healthcare
The Rise of Intelligent Assistants
- 1st Edition - March 2, 2026
- Latest edition
- Editors: Laxmi Shaw, Shubham Mahajan, Kamal Upreti
- Language: English
Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical application in health… Read more
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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.
- 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
Biomedical and Healthcare Informatics Researchers focused on AI-driven medical data analysis and healthcare innovation. Data Scientists and Machine Learning Engineers specializing in healthcare NLP applications and intelligent assistant development. Graduate and Postgraduate Students studying AI, computer science, health informatics, or biomedical engineering. Healthcare Technology Developers and Startups designing NLP-powered healthcare solutions and virtual assistants. Academic Faculty and Educators teaching AI, NLP, or healthcare technology courses
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
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
- Edition: 1
- Latest edition
- Published: March 2, 2026
- Language: English
LS
Laxmi Shaw
Dr. Laxmi Shaw is a Postdoctoral Scholar at Texas State University, specializing in adversarial machine learning, large language models, and healthcare fraud analytics. She previously volunteered as a Senior Postdoctoral Researcher at UT Austin’s Dell Medical School, focusing on predictive biomarker modeling and inflammation detection using HPC. With over six years of industry and research experience at Samsung R&D and Carrier Corporation, her expertise includes AI-driven product development, IoT analytics, and digital twin modeling.
Dr. Shaw earned her Ph.D. in Electrical Engineering (AI/ML) from IIT Kharagpur, India, and holds advanced degrees from Jadavpur and Sambalpur Universities. 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.
Affiliations and expertise
Senior Postdoctoral Fellow, Dell Medical School,, University of Texas at Austin, Texas, USASM
Shubham Mahajan
Dr. Shubham Mahajan, a distinguished member of prestigious organizations such as IEEE, ACM, and IAENG, boasts an impressive academic and professional background. He earned his B.Tech. degree from Baba Ghulam Shah Badshah University, his M.Tech. degree from Chandigarh University, and his Ph.D. degree from Shri Mata Vaishno Devi University (SMVDU) in Katra, India.
Dr. Mahajan has a remarkable track record in the field of artificial intelligence and image processing, holding an impressive portfolio of eleven Indian patents, as well as one Australian and one German patent. His contributions to the field are further evidenced by his extensive publication record, which includes over 100+ articles published in peer-reviewed journals, conferences and 10+ books. His research interests span a wide array of topics, encompassing image processing, video compression, image segmentation, fuzzy entropy, nature-inspired computing methods, optimization, data mining, machine learning, robotics, and optical communication. Notably, his dedication and expertise have earned him the 'Best Research Paper Award' from ICRIC 2019, published by Springer in the LNEE series.
In recognition of his exceptional achievements, Dr. Mahajan has received numerous accolades and honours throughout his career. These include the Best Student Award in 2019, the IEEE Region-10 Travel Grant Award in 2019, the 2nd runner-up prize in the IEEE RAS HACKATHON in 2019 (held in Bangladesh), the IEEE Student Early Researcher Conference Fund (SERCF) in 2020, the Emerging Scientist Award in 2021, and the IEEE Signal Processing Society Professional Development Grant in 2021. His commitment to excellence in research was further underscored by his receipt of the Excellence in Research Award in 2023.
Dr. Mahajan's impact extends beyond the realm of academia. He has served as a Campus Ambassador for IEEE, representing esteemed institutions such as IIT Bombay, Kanpur, Varanasi, Delhi, as well as various multinational corporations. His active engagement in fostering international research collaborations reflects his enthusiasm for advancing the frontiers of knowledge and innovation on a global scale.
Affiliations and expertise
Amity School of Engineering and Technology, Amity University Haryana., IndiaKU
Kamal Upreti
Dr. Kamal Upreti is an Associate Professor in the Department of Computer Science at CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He holds a B.Tech (Hons) from UPTU, an M.Tech (Gold Medalist), a PGDM (Executive) from IMT Ghaziabad, a Ph.D. in Computer Science & Engineering, and completed a postdoc at National Taipei University of Business, Taiwan, funded by MHRD.
With over 15 years of teaching, research, and corporate experience, Dr. Upreti has published 50+ patents, 32 magazine issues, 110+ research papers, and authored or edited 45+ books with publishers like CRC Press and Oxford. His expertise spans modern physics, data analytics, cybersecurity, machine learning, healthcare, embedded systems, and cloud computing.
He has worked with organizations including HCL, NECHCL, Hindustan Times, and various academic institutes. Notable projects include Japan’s “Hydrastore,” India’s Integrated Power Development Scheme (IPDS), and a significant ICMR-funded cardiovascular disease prediction project (₹80 Lakhs) in collaboration with GB Pant and AIIMS Delhi. He has secured funding from DST SERB (₹5 Lakhs) for ICSCPS-2024 and AICTE-IBIP (₹10 Lakhs) for 2024-2026.
Dr. Upreti frequently serves as a session chair, keynote speaker, corporate trainer, and faculty developer. He has been honored as Best Teacher, Best Researcher, and Gold Medalist in his M.Tech program.
Affiliations and expertise
Associate Professor, Department of Computer Science, Christ (Deemed to be University), Delhi-NCR Ghaziabad, Uttar Pradesh, India