This book showcases the potential of artificial intelligence to transform diagnostics, predictive analytics, personalized treatment, and healthcare resource optimization. It presents real-world applications and emerging trends, such as quantum computing, federated learning, and edge artificial intelligence.
Features:
- Explores the intersection of artificial intelligence and neuroscience, including applications in understanding brain disorders, cognitive functions, and neuroimaging data processing.
- Provides insights into emerging technologies such as quantum computing and their potential to revolutionize medical data processing, accelerate artificial intelligence algorithms, and solve previously intractable problems in healthcare.
- Analyzes advanced strategies for identifying and mitigating biases in artificial intelligence models, ensuring compliance with global healthcare regulations and fostering ethical artificial intelligence adoption.
- Discusses the application of multi-agent artificial intelligence systems in optimizing hospital operations, emergency response, and resource management in healthcare ecosystems.
- Covers the use of generative models, such as generative adversarial network and variational autoencoders, in drug design, protein structure prediction, and the creation of synthetic biomedical datasets.
The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, drug delivery, and information technology.
Table of Contents:
1. A Comparative Overview of Artificial Intelligence for Medical Data Processing. 2. AI-Powered Medical Data Processing From Algorithms to Intelligent Healthcare Systems. 3. Natural Language Processing in Healthcare: Transforming Clinical Texts with Context-Aware Systems. 4. A Review of AI-Driven Advances in Medical Imaging and Diagnostics. 5. AI-Driven Biomedical Image Processing for Breast Cancer Diagnosis: Techniques, Applications, and Clinical Integration. 6. Hybrid Deep Learning and Explainable AI for Histopathological Analysis and Cancer Biomarker Detection. 7. AI in Multi-Omics Data Integration for Precision Medicine. 8. Artificial Intelligence for EEG Biomarker Discovery in Emotional Disorders. 9. Reinforcement Learning in Personalised Healthcare and Decision Support. 10. Real-Time Data Fusion with Edge AI for Wearables and IoT Devices. 11. AI-Driven Predictive Analytics for Disease Forecasting, Management, and Prevention. 12. Generative AI in Drug Discovery and Biomedical Applications. 13. Fusion of Convolutional, Transformer, and Capsule Architectures for Enhanced Liver Tumour Segmentation. 14. Artificial Intelligence Applications in Rural and Remote Healthcare Delivery. 15. Ethical AI in Healthcare: Addressing Bias, Fairness, and Regulatory Compliance. 16. Future horizon: Quantum computing and AI medical innovation. 17. Concluding Perspectives: Consolidated Insights, Open Challenges, and Recommendations
About the Author :
Chetna Sharma is working as an Assistant Professor in Chitkara University, Punjab. Her areas of expertise include Machine Learning, Soft Computing, Pattern Recognition, Image Processing, and Artificial Intelligence.
Amandeep Kaur is currently working as a Professor at Chitkara University Institute of Engineering and Technology, Chitkara University Punjab, India. Her main areas of research are medical informatics, machine learning, IoT, artificial intelligence, and cloud computing.
Harpreet Singh is a distinguished Professor of Electrical and Computer Engineering at Wayne State University. His research interests include diverse and include computers, software engineering, and intelligent systems.
