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Full Description
Next-Gen Healthcare: AI-Powered Medical Innovations surveys how AI is transforming medicine from core methods to clinical deployment, emphasizing that technological power must be balanced with resilience, safety, and ethics. Part I covers the foundations of trustworthy medical AI, including adversarial defenses, cross-population generalization, optimized fine-tuning, interpretability for high-stakes decisions, and frontiers like quantum neural networks, ending with large language models focused on error detection, privacy, and patient safety.
Part II applies AI methods to real-world domains: early Alzheimer's and dementia diagnosis, oncology via lymphoma response prediction, chronic disease management with diabetes stratification and retinopathy detection, and extensions into dermatology, obesity, and mental health. The book's introduce safeguards first, then generalizable and cutting-edge methods, ending with disease-focused AI applications. Its philosophy that safety underpins innovation and clinical impact, offering both a map of today's medical AI and a guide to future opportunities
Contents
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.
