Generative AI and Internet of Medical Things

Artificial Intelligence, Biomedical Engineering, Wearable Sensors

Pethuru Raj Chelliah, Satya Prakash Yadav, Victor Hugo C. de Albuquerque, H. Herbert Song

• Publisher: De Gruyter
• Edition: 1
• Year: 2026
• Topic: Computer Science
• Print ISBN:
  9783112237076
• Subtopic: Artificial Intelligence (AI) & Semantics
• eBook ISBN:
  9783112237083
• Index: Computer Science

The Internet of Medical Things (IoMT) paradigm focuses on digitizing medical electronics and instruments in hospital environments, connecting them to cloud-hosted healthcare-related content, data, services, and applications through the Internet communication infrastructure. This book also illustrates how such a technological empowerment makes healthcare devices and equipment intelligently autonomous in their decisions, actions, and reactions. It elucidates the latest trends and transitions in the field of IoMT for medical doctors, surgeons, specialists, caregivers, and patients. It covers algorithms used in machine learning, deep learning, reinforcement learning and ensemble learning, as well as the individual steps involved in AI model development, and explains how to analyse healthcare data effectively.  Additionally, generative AI tools -including large, small, vision, and multimodal language models -are useful for envisioning and implementing expert decision science, question/answer (Q/A) systems, and conversational AI chatbots to support medical professionals in their work. This book is a handholding guide for healthcare service providers and consumers, AI engineers, 5G, edge computing and IoT architects.

Table of contents

• Contents
• Preface
• List of contributors
• Chapter 1 `Demystifying the Generative AI Paradigm
• Chapter 2 Sensor Fusion Methods
• Chapter 3 Multispectral Image Feature Extraction
• Chapter 4 Multifaceted Wearable Sensor Technologies for Chronic Disease Management
• Chapter 5 Smart Healthcare Services and Applications
• Chapter 6 Event-Driven and Microservices Architecture (EDA and MSA)
• Chapter 7 Computer Vision (CV) and Natural Language Processing (NLP)
• Chapter 8 Applications of Machine Learning in Real-Time Monitoring and Diagnosis of Chronic Diseases
• Chapter 9 Wearable Sensors for Early Detection of Cardiovascular Diseases
• Chapter 10 Machine Learning-Based Prediction Models for Diabetes Management Using Wearable Sensors
• Chapter 11 Real-Time Monitoring and Diagnosis of Chronic Respiratory Diseases with Wearable Sensors
• Chapter 12 Wearable Sensor Technologies for Chronic Pain Management
• Chapter 13 Machine Learning Techniques for Real-Time Detection of Neurological Disorders Using Wearable Sensors
• Chapter 14 Challenges and Opportunities in Implementing Wearable Sensor-Based Systems for Chronic Disease Management
• Chapter 15 Smart Wearables for Real-Time Monitoring and Rehabilitation of Musculoskeletal Disorders
• Chapter 16 Wearable Sensors for Remote Monitoring and Management of Chronic Renal Diseases
• Chapter 17 Machine Learning Algorithms for Predicting Disease Progression and Personalized Treatment in Chronic Diseases
• Chapter 18 Wearable Sensors for Real-Time Monitoring of Mental Health Disorders
• Chapter 19 Real-Time Monitoring of Chronic Gastrointestinal Diseases with Wearable Sensors
• Chapter 20 Human-Centered Design of Wearable Sensor Technologies for Chronic Disease Management
• Chapter 21 Validation and Accuracy of Wearable Sensor Data in Chronic Disease Monitoring and Diagnosis
• Index
• Mathematical Methods in the Digital Age