| ISBN13: | 9780443239793 |
| ISBN10: | 0443239797 |
| Kötéstípus: | Puhakötés |
| Terjedelem: | 400 oldal |
| Méret: | 235x191 mm |
| Nyelv: | angol |
| 700 |
Explainable AI in Healthcare Imaging for Medical Diagnoses
EUR 189.99
Kattintson ide a feliratkozáshoz
In an era where Artificial Intelligence (AI) is revolutionizing healthcare, Explainable AI in Healthcare Imaging for Precision Medicine addresses the critical need for transparency, trust, and accountability in AI-driven medical technologies. As AI becomes an integral part of clinical decision-making, especially in imaging and precision medicine, the question of how AI reaches its conclusions grows increasingly significant. This book explores how Explainable AI (XAI) is transforming healthcare by making AI systems more interpretable, reliable, and transparent, empowering clinicians and enhancing patient outcomes. Through a comprehensive examination of the latest research, real-world case studies, and expert insights, this book delves into the application of XAI in medical imaging, disease diagnosis, treatment planning, and personalized care. It discusses the technical methodologies behind XAI, the challenges and opportunities of its integration into healthcare, and the ethical and regulatory considerations that will shape the future of AI-assisted medical decisions. Key areas of focus include the role of XAI in improving diagnostic accuracy in fields such as radiology, pathology, and genomics and its potential to enhance collaboration between AI systems, healthcare professionals, and patients. The book also highlights practical applications of XAI in personalized medicine, showing how explainable models help tailor treatments to individual patients, and discusses how XAI can contribute to reducing bias and improving fairness in medical decision-making. Written by leading experts in AI, healthcare, and precision medicine, Explain[S3G1] able AI in Healthcare Imaging for Precision Medicine is an essential resource for researchers, clinicians, students, and policymakers. Whether you are looking to stay at the forefront of AI innovations in healthcare or seeking to understand how explainability can build trust in AI systems, this book provides the insights and knowledge needed to navigate the evolving landscape of AI in medicine. It invites readers to explore how XAI can revolutionize healthcare and precision medicine, shaping a future where AI is both powerful and trustworthy.
- Provides step-by-step procedures to build a digital human model
- Assists in validating predicted human motion using simulations and experiments
- Offers formulation optimization features for dynamic human motion prediction
2. XAI implementation in traditional alternate medicine system
3. Explainable Computational Intelligence in Bio and Clinical Medicine
4. Enhancing Medical AI Interpretability Using Heatmap Visualization Techniques
5. An interpretation-model-guided classification method for malignant pulmonary nodule
6. Case Studies: Explainable AI for Healthcare 5.0
7. OML-GANs: An Optimized Multi-Level Generative Adversarial Networks Model for Multi-Omics Cancer Subtype Classification
8. Explainable Artificial Intelligence in Epilepsy Management: Unveiling the Model Interpretability
9. Revolutionizing Cancer Diagnosis with AI-Enhanced Histopathology and Deep Learning: A Study on Enhanced Image Analysis and Model Explainability
10. Unveiling Explainable Artificial Intelligence (XAI) in Advancing Precision Medicine: An Overview
11. Pneumonia and Brain Tumors Diagnosis Using Machine Learning Algorithms
12. Explainable Artificial Intelligence in Medical Research: A Synopsis for Clinical Practitioners - Comprehensive XAI Methodologies
13. Advancing Explainable AI and Deep Learning in Medical Imaging for Precision Medicine and Ethical Healthcare
14. Leveraging Explainable AI in Deep Learning for Brain Tumor Detection
15. Unveiling the Root Causes of Diabetes Using Explainable AI
16. Explainable AI for Melanoma Diagnosis through Dermosopic Images: Recent Findings and Future Directions
17. Enhancing Multi-Omics Cancer Subtype Classification Using Explainable Convolutional Neural Networks
18. Explainable Convolutional Neural Network for Parkinson’s Disease Detection
19. Data analytics and cognitive computing for digital health: A Generic Approach and a review of emerging technologies, challenges, and research directions
20. New challenges and opportunities to explainable artificial intelligence (XAI) in smart healthcare
