Main description:

Since its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today's intelligent systems compete with human capabilities in medical tasks. However, advanced use of artificial intelligence causes intelligent systems to be black-box. That situation is not good for building trustworthy intelligent systems in medical applications. For a remarkable amount of time, researchers have tried to solve the black-box issue by using modular additions, which have led to the rise of the term: interpretable artificial intelligence. As the literature matured (as a result of, in particular, deep learning), that term transformed into explainable artificial intelligence (XAI).

This book provides an essential edited work regarding the latest advancements in explainable artificial intelligence (XAI) for biomedical applications. It includes not only introductive perspectives but also applied touches and discussions regarding critical problems as well as future insights.

Topics discussed in the book include:

XAI for the applications with medical images
XAI use cases for alternative medical data/task
Different XAI methods for biomedical applications
Reviews for the XAI research for critical biomedical problems.

Explainable Artificial Intelligence for Biomedical Applications is ideal for academicians, researchers, students, engineers, and experts from the fields of computer science, biomedical, medical, and health sciences. It also welcomes all readers of different fields to be informed about use cases of XAI in black-box artificial intelligence. In this sense, the book can be used for both teaching and reference source purposes.

Contents:

1. Gastric Cancer Detection Using Hybrid Based Network and SHAP Analysis 2. LIME Approach in Diagnosing Diseases: A Study on Explainable AI 3. Explainable Artificial Intelligence (XAI) in the Veterinary and Animal Sciences Field 4. Interpretable Analysis of the Potential Impact of Various Versions of Corona Virus: A Case Study 5. XAI in Biomedical Applications 6. What Makes the Survival of Heart Failure Patients: Prediction by the Iterative Learning Approach and Detailed Factor Analysis with the SHAP Algorithm 7. Class Activation Mapping and Deep Learning For Explainable Biomedical Applications 8. Pragmatic Study of IoT In Healthcare Security with an Explainable AI Perspective 9. Chest Disease Identification from X-rays Using Deep Learning 10. Explainable Artificial Intelligence Applications in Dentistry: Theoretical Research 11. Application of Explainable Artificial Intelligence in Drug Discovery and Drug Design 12. Automatic Segmentation of Spinal Cord Gray Matter from MR Images Using U-Net Architecture 13. XAI for Drug Discovery 14. Explainable Intelligence Enabled Smart Healthcare for Rural Communities 15. Explainable Artificial Intelligence on Drug Discovery for Biomedical Applications 16. XAI in Hybrid Classification of Brain MRI Tumor Images 17. Comparative Analysis of Breast Cancer Diagnosis Driven by a Smart IoT Based Approach