Introduction

Recently, the entire world faced complex health challenges like pandemics situations such as covid-19, mpox, aging populations, chronic diseases etc. The traditional methods of the healthcare sector are struggling in terms of growing demands, needs of patients and healthcare providers. Hence, there is an urgent and ongoing need for innovation in the healthcare sector to deal with such challenges. In this context, Healthcare 5.0 can emerge as a new paradigm that can offer transformative solutions based on cutting-edge technologies like Artificial Intelligence (AI), Machine Learning (ML), the Internet of Medical Things (IoMT), and Big Data. Healthcare 5.0 can be described as the next generation of healthcare that can integrate advanced digital technologies like AI, ML, IoMT to enhance patient care, optimize operations, and improve health outcomes. This paradigm shifts the healthcare sector towards data driven, interconnected and patient centric approach. Other hand, Healthcare 5.0 aims also to improve the diagnostic accuracy, streamlined operations and personalized treatment. In Healthcare 5.0, AI techniques can be adopted for analysing the medical images with improved accuracy. IoMT devices can be utilized to monitor the health of the patients in a real time environment. ML techniques can be used to determine the individual patient's need and treatment. The predictive care trends can be identified using big data analytics. Moreover, Large Language Models (LLMs) are transforming Healthcare 5.0 by enhancing data-driven decision-making, automation, and personalized patient care. In clinical decision support, LLMs assist doctors by analysing vast medical literature, identifying patterns, and suggesting treatment options. Medical image interpretation is enhanced by integrating LLMs with AI-powered Furthermore, LLMs support real-time patient monitoring and predictive analytics, helping detect early signs of diseases and recommending preventive measures. Hence, the aim of this book is to bridge the knowledge gap and also provide information on the cutting-edge technologies for navigating in the Healthcare 5.0 landscape. It also offers practical insights into AI, ML, IoMT, and Big Data including capabilities of LLMs and its applicability for solving the real-world healthcare challenges. The detailed case studies also provide the insight behind the implementations to the readers. This book also explores future trends, and ongoing transformation in the healthcare industry.

Objective

The main objective of this book is to focus on the latest techniques adopted in the field of healthcare and significance of the healthcare 5.0. In healthcare 5.0 era, informatics, machine learning, big data and IoMT based techniques play a significant role for disease diagnosis and their prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data like ECG data, X-Ray data, image data etc. Thus, this book will focus on the usability of machine learning; artificial intelligence, big data and IoMT based techniques to handle structured and unstructured data. This book also emphasizes the significance of telemedicine and remote care in the context of healthcare 5.0. It also covers the usages of the personalized healthcare system for better outcomes. This book also considers the application of the large language model for healthcare 5.0 as of now without LLMs, healthcare 5.0 not achieved.  

Target Audience

Scientist, researchers, practitioners and academicians in higher education institutions including universities and vocational colleges, data analyst, physicians are primary readers.  Moreover, the researchers and practitioners interested in the role of AI, ML, Big Data, IoMT in healthcare can also be considered as secondary audience for this book. 

Recommended Topics

Theme 1: Artificial Intelligence (AI) and Machine Learning (ML) for Healthcare 5.0

Theme 2: Role of Big Data Analytics in Disease Diagnosis and Prediction 

Theme 3: Internet of Medical Things (IoMT) for Innovative Healthcare System 

Theme 4: Personalized HealthCare Assisted System

Theme 5: Usability and Significance of Telemedicine and Remote Care Healthcare 5.0

Theme 6: Foundations of Large Language Models in Healthcare 

These above-mentioned themes consist of the following topics in healthcare 5.0, but not limited to: 

·       Personal Health Record based Predictive Diagnostic Systems

·       Intelligent E-Health Disease Diagnostic Systems

·       Significance of Attribute Weighting Methods in Medical Informatics

·       Clinical Decision Support Systems

·       Ontology based Medical Systems

·       Expert Systems for Disease Diagnosis

·       Ensemble Classifiers for Disease Prediction

·       Deep Learning and Extreme Machine Learning in Healthcare

·       Predictive Analytic Techniques for Health Data

·       Big Data Techniques in Healthcare -Issues, Challenges and Need

·       Management of Big Data in Healthcare

·       IOT and Cloud based Solutions for Healthcare

·       Monitoring of Elderly & Remote Patients using IoT Based Approach

·       Privacy Preservation Techniques for Healthcare Data- Need, Issues and Potential Solutions

.       Reviews and case studies in Machine learning, Big Data, IOT and Privacy Preservation Techniques for Medical  Informatics 

. Telemedicine and Remote Care Healthcare 5.0

. IOMT Solutions in Healthcare 5.0

. Personalized HealthCare Assisted Systems

. Real-Time Monitoring and Decision Support in Personalized Healthcare Assistance Systems  

Submission Procedure

Interested contributors must submit the tentative title of the chapter along with abstract in a single page with full list of authors to any of the editors through email before August 10, 2025. The contributors will be notified about the acceptance of the abstract based on the editors review assessment on or before August 25, 2025.

Researchers and practitioners are invited to submit their high quality full chapter (approximately approximately 10,000 words) through Elsevier-Electronic Manuscript Submission Systems on or before March 31, 2026. The final decision for the submitted full book chapters will be rendered on or before April 30, 2026.

All submissions must be original and should not be under review by another publication. Submitted chapters will be refereed by at least two independent and expert reviewers for quality, correctness, originality, and relevance. Authors should make sure that their submission has 15% or lower similarity as per iThenticate or Turnitin and AI similarity should be below threshold. Contributors may also be requested to serve as reviewers for this project. 

In addition, the Elsevier policies about AI can be found at the following links: 

1. https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier

2. https://www.elsevier.com/about/policies-and-standards/publishing-ethics-books/the-use-of-generative-ai-and-ai-assisted-technologies-in-the-editing-process

Kindly visit the above links while preparing your book chapters.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication. All manuscripts are accepted based on a double-blind peer review editorial process.  

Publisher

This book is scheduled to be published by Elsevier under the imprint Academic Press, a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance for the benefit of humanity. This publication is anticipated to be released in August 2026.

Important Dates

Abstract submission due: September 20, 2025

Notification about Abstract: October 10, 2025

Full chapter submission due:   March 15, 2026

Notification to authors:  April 15, 2026

Camera-ready submission: Due Within One Month After Outcome of First Review Notified to Authors

Final book content to be submitted  to the publisher: On or Before May 31, 2026

Inquiries and Communications

• Prof. Pardeep Kumar, Department of Computer Science & Engineering, Jaypee University of Information Technology, Solan, India Email: pardeepkumarkhokhar@gmail.com

• Dr. Yugal Kumar, Associate Professor, NMIMS University Chandigarh, School of Technology Management and Engineering, Sarangpur, Chandigarh 400 056, India

Email: yugalkumar.14@gmail.com

• Prof. Ming Dong,  Department of Computer Science, Wayne State University, Detroit, MI 48202, United States

Email: mdong@wayne.edu