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This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.
Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.
This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.
Table des matières
Chapter 1: Artificial Intelligence for Healthcare Applications: A Review. Chapter 2: Open Problems of XAI Especially for Medical Domain. Chapter 3: Explainable AI in Biomedical Applications: Vision, Framework, Anxieties, and Challenges. Chapter 4: XAI in Drug Discovery. Chapter 5: The Use of Explainable Artificial Intelligence in Medical Image Processing: A Research Study. Chapter 6: Current Progress and Open Research Challenges for XAI in Deep Learning Across Medical Imaging. Chapter 7: From Black Boxes to Transparent Machines: The Quest for Explainable AI. Chapter 8: XAI and Disease Diagnosis. Chapter 9: Explainability and the Role of Digital Twins in Personalized Medicine and Healthcare Optimization. Chapter 10: XAI for Trustworthiness in Medical Tourism. Chapter 11: XAI for Advancements in Drug Discovery. Chapter 12: A Hybrid Explainable Artificial Intelligence Approach for Anti-Cancer Drug Discovery: Exploring the Potential of Explainable Artificial Intelligence in Computational Biology
A propos de l'auteur
Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey, and Visiting Researcher in University of North Dakota, USA. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, biomedical applications, optimization, the chaos theory, distance education, e-learning, computer education, and computer science.
Nilgun Sengoz is an Assistant Professor in Burdur Mehmet Akif University, Turkey. Her areas of interest are artificial intelligence, machine learning and deep learning, medical image processing and also human computer interaction.
Xi Chen is a Senior Software Engineer in Meta, Burlingame, CA, USA. He graduated from the University of Kentucky focusing in bioinformatics PhD and Statistics MA. He is passionate about Big Data, Machine Learning and AI research, with strong interpersonal skills, adept at working in teams and successfully delivering projects.
Jose Antonio Marmolejo is a Professor at National Autonomous University of Mexico, Mexico. His research is on operations research, largescale optimization techniques, computational techniques, analytical methods for planning, operations, and control of electric energy and logistic systems, sustainable supply chain design and digital twins in supply chains.
Résumé
This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.
