Natural Language Processing in Healthcare Informatics

Challenges and Future Directions

A. Shankar, Saranya Jayapalan, Basant. K. Tiwary

• Publisher: Bentham Science Publishers
• Year: 2026
• Topic: Computer Science
• eBook ISBN:
  9798898814922
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Natural Language Processing in Healthcare Informatics: Challenges and Future Directions is an exploration into the transformative role of NLP and AI technologies in modern healthcare systems. The book delves into foundational concepts, advanced deep learning techniques, and cutting-edge applications of NLP in clinical decision support, electronic health record analysis, medical literature mining, patient-provider communication, and personalised medicine.

Structured across ten detailed chapters, the volume covers both theoretical foundations and practical implementations. Early chapters introduce AI and NLP in healthcare, highlighting applications in telehealth, wearable technologies, robotic surgery, and FDA-approved AI devices. Subsequent chapters examine challenges unique to healthcare NLP, including data quality and standardisation, linguistic variability, computational limitations, and ethical considerations such as bias, transparency, and patient privacy.

The book also provides in-depth insights into deep learning approaches for medical text analysis, preprocessing and annotation strategies, the evaluation of large language models for RNA interactions, and the application of Bi-LSTM architectures for advanced healthcare NLP tasks. Innovative chapters explore the role of knowledge graphs in rare disease prediction, AI and ML for focused ultrasound treatments, mathematical modelling using fuzzy numbers for healthcare NLP trends, and AI/ML integration in drug discovery and personalised medicine.

Key Features:

-Detailed discussions and case studies on the applications of AI and NLP in healthcare, including telehealth, EHR analysis, and medical imaging.


-In-depth coverage of challenges in healthcare NLP encompassing data quality, linguistic variability, computational requirements, and ethical considerations.


-Explores deep learning techniques: RNNs, CNNs, Transformers, Bi-LSTM, and their application to medical text.


-Gives practical guidance on data preprocessing, annotation, and evaluation for healthcare NLP tasks.


-Provides insight into emerging trends by integrating theoretical concepts with real-world implementations, coding examples, and model workflows in Python and MATLAB.

Table of contents

• Welcome
• Table of Contents
• Title
• BENTHAM SCIENCE PUBLISHERS LTD.
• FOREWORD
• Preface
• List of Contributors
• Introduction to Artificial Intelligence and Natural Language Processing in Healthcare
• Challenges in Natural Language Processing for Healthcare Informatics
• Deep Learning Techniques for Medical Text Analysis
• Data Preprocessing and Annotation for NLP in Healthcare
• Evaluating Large Language Models for RNA Interaction Extraction: A Comparative Study on Biomedical Research
• Futuristic Approach for Enhancing Healthcare Using Bi-LSTM and NLP
• Role of Knowledge Graph in Rare Disease Prediction
• Leveraging AI and ML to Enhance Focused Ultrasound Treatments for Essential Tremors
• A Mathematical Model for Future Trends and Directions in Healthcare NLP Using Ranking of Tetradecagonal Fuzzy Numbers
• Innovative Approaches in Drug Discovery: Target Identification, Computational Strategies, and Emerging Trends in ML and AI Integration
• AI and NLP in Drug Discovery and Target Identification: Focus on COVID-19
• AI-Assisted Public Health Surveillance of Gut Microbiome Health
• Advancements in Transformer-Based Models for NLP in Healthcare
• Revolutionizing Mental Health Care with AI-Driven NLP Chatbots: Innovations, Applications, and Future Directions
• Emerging Technologies in NLP for Healthcare
• Navigating the Future of Healthcare: Exploring the Next Phase in NLP