
Revolutionizing Medical Systems using Artificial Intelligence
A Breakthrough in Healthcare
- 1st Edition - January 24, 2025
- Imprint: Academic Press
- 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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Request a sales quoteThis 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.
- Provides a wide range of coverage for various prediction and segmentation algorithms that are based on machine learning and deep learning technology
- Covers various predictive and segmentation algorithms exploited by various medical personnel to improve the accuracy of treatment to the patients
- Highlights improvements in quality and efficiency of medical decision-making in the early detection of critical diseases using AI
- 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
- Edition: 1
- Published: January 24, 2025
- Imprint: Academic Press
- No. of pages: 334
- Language: English
- Paperback ISBN: 9780443328626
- eBook ISBN: 9780443328633
AK
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.
DS
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.