Revolutionizing Medical Systems using Artificial Intelligence

A Breakthrough in Healthcare

1st Edition - January 24, 2025
Editors: Ashish Kumar, Divya Singh
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 8 6 2 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 2 8 6 3 - 3

Revolutionizing Medical Systems using Artificial Intelligence: A Breakthrough in Healthcare provides an overview of various machine learning and deep learning techniques, addres… Read more

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Revolutionizing Medical Systems using Artificial Intelligence: A Breakthrough in Healthcare provides an overview of various machine learning and deep learning techniques, addressing the needs of patients and the necessity for medical aid at early stages of disease. The book reviews, analyzes, and compares the different methodologies utilized for the prediction and detection of diseases. In addition, it explores the possible deployment of these advancements in medical systems, from paper to practice. With the advent of technology, tele consultation, telemedicine, and mobile health care, it is now possible to provide continuous monitoring of the health of patients.

This title fulfills the needs of connected healthcare systems, providing insights into the role of Artificial Intelligence in the prognosis, diagnosis, and analysis of several diseases. It will be a valuable resource for health professionals, scientists and researchers, health practitioners, students, and all those who wish to broaden their knowledge in the challenging field of artificial intelligence in medical systems and diseases.

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of contributors
1. Healthcare management using artificial intelligence
Abstract
1.1 Introduction
1.2 Managerial and organizational orientation of information and analytical support in healthcare
1.3 Conclusions
References
2. Background and research orientation of artificial intelligence models for segmentation in medical imaging
Abstract
2.1 Introduction
2.2 Innovations and breakthroughs in artificial intelligence based segmentation: Overcoming challenges
2.3 The importance of segmentation in medical imaging
2.4 Evolution of artificial intelligence in medical imaging segmentation
2.5 Integration with clinical workflow
2.6 Procedure for evolution of artificial intelligence in medical imaging segmentation
2.7 Collaboration and interdisciplinary research
2.8 Generative models and adversarial training
2.9 Challenges
2.10 Future prospects
2.11 Conclusion
References
3. Benchmark image and clinical datasets for analysis in the medical system
Abstract
3.1 Introduction
3.2 Biomedical image analysis dataset
3.3 Clinical health care dataset
3.4 Conclusion and future work
Reference
4. Proactive prognosis: Predicting the course of mental and neurological disorders with artificial intelligence
Abstract
4.1 Introduction
4.2 Understanding artificial intelligence-based prognosis for mental and neurological diseases
4.3 Benefits and challenges of artificial intelligence-based prognosis
4.4 Ethical considerations and implementation strategies
4.5 The future of artificial intelligence-based prognosis
4.6 Conclusion
References
5. Exploring brain tumor detection through artificial intelligence
Abstract
5.1 Introduction
5.2 Understanding brain tumors
5.3 Role of artificial intelligence in brain tumor detection
5.4 Brain tumor detection utilizing artificial intelligence based approaches
5.5 Conclusion and future directions
References
6. Empowering early detection: artificial intelligence as a tool for breast cancer diagnosis
Abstract
6.1 Introduction
6.2 Breast imaging techniques: Traditional versus modern approaches
6.3 Deep dive in deep learning based models for breast cancer
6.4 Transformer-based model for breast cancer
6.5 Datasets
6.6 Importance of data quality and management
6.7 Comparative analysis of evaluation metrics and datasets used
6.8 Benefits of using artificial intelligence with existing imaging techniques
6.9 Challenges in artificial intelligence implementation in medical imaging
6.10 Limitations of current artificial intelligence models and future directions for research
6.11 Conclusion
References
7. Artificial intelligence based disease diagnosis using ultrasound imaging
Abstract
7.1 Introduction
7.2 Artificial intelligence based ultrasound-driven cardiovascular disease risk assessment
7.3 Deep learning for cardiovascular disease risk stratification using ultrasound
7.4 Deep learning for plaque wall segmentation and carotid image-based phenotype measurement
7.5 Radiomic feature estimation via plaque wall segmentation in the U-shaped network framework
7.6 Conclusion
References
8. Growth optimization-based stacked bidirectional long short-term recurrent neural network model for detecting breast cancer in Internet of Things healthcare environment
Abstract
8.1 Introduction
8.2 Related works
8.3 Proposed model
8.4 Results and discussion
8.5 Conclusion
References
9. Detection of depression by utilizing late fusion of sequential actigraphy features
Abstract
9.1 Introduction
9.2 Problem statement
9.3 Literature review
9.4 Data preprocessing and feature extraction of long short-term memory
9.5 Methodology
9.6 Results
9.7 Conclusion
9.8 Future enhancements
References
10. Developmental pediatrics progression matched with artificial intelligence: A growth perspective in healthcare
Abstract
10.1 An introduction to pediatrics and its structure
10.2 Technological advancements in developmental pediatrics
10.3 Generative artificial intelligence in healthcare: A global perspective
10.4 Artificial intelligence and development pediatrics
10.5 Essentials and benefits of artificial intelligence in pediatrics
10.6 Challenges of artificial intelligence application in healthcare
10.7 Conclusion
References
11. Artificial intelligence in precision oncology: advances in cancer treatment
Abstract
11.1 Introduction
11.2 Challenges in cancer genomics data interpretation
11.3 Multiomics significance in the diagnosis and cancer treatment
11.4 Artificial intelligence technology usage in oncology
11.5 Biggest challenge in precision oncology: Data integration
11.6 Data selection from the input and preprocessing
11.7 Algorithm/prediction model selection and data integration
11.8 Testing the prediction models
11.9 Various learning algorithms, neural networks, and decision-making tools
11.10 Neural networks
11.11 Decision tools and learning models
11.12 Cancer mutation detection through machine learning
11.13 Artificial intelligence and precision oncology’s therapeutic benefits
11.14 Challenges and limitation
11.15 Conclusion
References
12. Impact of artificial intelligence bias in medical systems
Abstract
12.1 Introduction
12.2 Understanding artificial intelligence bias in medical systems
12.3 Types of bias in medical systems
12.4 Impact of bias in medical systems
12.5 Identifying and mitigating bias
12.6 Ethical considerations and future directions
12.7 Conclusion
References
13. A futuristic aspect towards modern healthcare system facilitated through artificial intelligence: A comprehensive perspective
Abstract
13.1 Introduction to healthcare system: An evolutionary perspective
13.2 Technological advancement in the healthcare system
13.3 Application of technology in healthcare system: A paradigm shift in techno-social context
13.4 Application of artificial intelligence in revolutionizing modern healthcare
13.5 Challenges in sustaining and implementing AI-based healthcare system
13.6 Scope and way ahead for artificial intelligence in healthcare
13.7 Conclusion
References
14. Satelliting traditional to smart healthcare advancement using artificial intelligence: A paradigm shift routing futuristic slant redefining
Abstract
14.1 Introduction
14.2 Healthcare artificial intelligence’s evolution
14.3 Applications of artificial intelligence in healthcare
14.4 Ethical issues and difficulties
14.5 Artificial intelligence advancements in healthcare aspects
14.6 Hospital and medical system: precision healthcare
14.7 Regulatory frameworks
14.8 Robotics integration: redefining healthcare treatment
14.9 Conclusion and future scope
References
15. Welcoming the evolution of healthcare: A transformative change through artificial intelligence and robotics
Abstract
15.1 Introduction
15.2 Artificial intelligence for predicting cardiovascular disease risk
15.3 Personalized cancer treatment optimization with artificial intelligence
15.4 Robot-supported surgery
15.5 Future of healthcare and artificial intelligence
15.6 Conclusion
References
Index

Ashish Kumar

Dr. Ashish Kumar, Ph.D., is working as an associate professor with Bennett University, Greater Noida, U.P., India till date. He has worked with Bharati Vidyapeeth’s College of Engineering (Affiliated to GGS Indraprastha University) from Aug 2009 to Jul 2022. He has completed his Ph.D. in Computer Science and Engineering from Delhi Technological University (formerly DCE), New Delhi, India in 2020. He received the Best Researcher award from the Delhi Technological University for his contribution to the computer vision domain. He has completed M. Tech with distinction in Computer Science and Engineering from GGS Indraprastha University, New Delhi. He has published more than 25 research papers in various reputed national and international journals and conferences. He has published 15+ book chapters in various Scopus-indexed books. He has authored/edited several books in AI, computer vision, and healthcare domains with reputed publishers. He is an active member of various international societies and clubs. He is a reviewer with many reputed journals and in the technical program committee of various national/ international conferences. Dr. Kumar also served as a session chair at many international and national conferences. His current research interests include object tracking, image processing, artificial intelligence, and medical imaging analysis.

Affiliations and expertise

Associate Professor, Bennett University, Greater Noida, U.P., India

Divya Singh

Dr. Divya Singh is currently working as an assistant professor with the School of Computer Science Engineering and Technology, Bennett University, Greater Noida. She holds the degree of B.Tech in information technology and an M.Tech in Computer Science Engineering. She has completed Ph.D. degree in Computer Science Engineering and Technology from IIT, BHU, Varanasi. She has published research publications in various reputed international journals, conferences and patent. Her research interests include the areas of Image and Video Processing, Computer Vision, Artificial Intelligence (AI), Machine Learning and Deep learning.

Affiliations and expertise

Associate Professor, Bennett University, Greater Noida, U.P., India

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