Next-Gen Healthcare

Nour Eldeen M. Khalifa received his B.Sc., M.Sc., and Ph.D. degree in 2006, 2009 and 2013 respectively, all from Cairo University, Faculty of Computers and Artificial Intelligence, Cairo, Egypt. He also had a Professional M.Sc. Degree in Cloud Computing in 2018. He authored/coauthored more than 50 publications and 4 edited books. He had more than 4000 citations. His name had been consecutively listed among Stanford University's top 2% of global scholars (2022-2023-2024). He had the Encouraging State Award (Egypt) in the Field of Engineering Science in 2024. He maintained significant editorial responsibilities as a board member for the Journal of World Science, Medical Data Mining journal, Academic Editor for PLOS One Journal, and reviewer for multiple international journals. Currently, he is an associate professor at Faculty of Computers and Artificial Intelligence, Cairo University. His research interests include wireless sensor networks, cryptography, multimedia, network security, machine, and deep learning. Mohamed Hamed N. Taha received the B.Sc., M.Sc., and Ph.D. degrees from the Faculty of Computers and Artificial Intelligence, Cairo University, in 2006, 2009, and 2013, respectively. He has been an Associate Professor with the Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, since 2016. He is a Reviewer of the IEEE internet of things journal. Deep learning, machine learning, the Internet of Things, wireless sensor networks, and blockchain are his research interests.

• Chapter 1  Adversarial Threats in Healthcare: A Comprehensive Analysis of Vulnerabilities, Defense Mechanisms, and Recent Research.- Chapter 2 Masked Autoencoder-Based Domain Adaptation for Cross-Population Breast-Lesion Classification in Mammograms.- Chapter 3 Optimized Block-Wise Fine-Tuning of VGG Models for Accurate and Explainable Detection of Chest Infectious Diseases Using Chest X Rays.- Chapter 4 Quantum Neural Network for Robust Image Classification: Applications to Medical and Benchmark Datasets.-  Chapter 5 Explainable Machine Learning Approaches for Cardiovascular Disease Detection: A Comparative Study on the UCI Heart Disease Dataset.- Chapter 6 Large Language Models (LLMs) in Medical Error Detection and Correction: A Comprehensive Review.- Chapter 7 Data Privacy and Security in Large Language Models for Medical Fields.- Chapter 8 Advancing Early Alzheimer's Diagnosis with Deep Learning on MRI Data.- Chapter  9 Predictive Models for Early Detection and Prognosis of Dementia using Artificial Intelligence and Machine Learning.- Chapter 10 Predicting Drug Response in Diffuse Large B-Cell Lymphoma Patients Using Machine Learning Models.- Chapter 11 Guideline-Concordant Two-Stage AI for Diabetes Severity Stratification and Pharmacotherapy Recommendation.- Chapter 12 MOBPITL: Enhancing Diabetic Retinopathy Detection via PiTMobileNetV2 Fusion and Lamb Optimization.- Chapter 13 A Comprehensive Approach to Skin Lesion Classification using Machine and Deep Learning.- Chapter 14 Next-Gen Diagnostics: Utilizing AI Classification Algorithms for Enhanced Obesity Detection and Intervention.- Chapter 15 Using Explainable AI for Assessment of Depression: A Systematic Literature Review.