This is a comprehensive book that explores how explainable artificial intelligence (XAI), particularly large language models (LLMs), is transforming healthcare. The book covers foundational concepts of XAI, emphasizing the need for transparency, accountability, and interpretability in AI-driven medical systems, that are crucial for clinician and patient trust. It examines the principles and methodologies in explainable AI. It details how LLMs can make complex machine learning outputs understandable through explanations, model design, and human-centered description.
Part of the book is dedicated to real-world applications, such as disease diagnosis, treatment planning, and patient management. It demonstrates how XAI improves clinical decision-making and patient outcomes. It discusses the integration of explainable LLMs into electronic health records (EHRs) and clinical workflows. It shows how these technologies facilitate data analysis, improve documentation, and support care. The book also addresses the challenges and limitations of deploying explainable LLMs in healthcare. It includes issues of privacy, data complexity, and adapting models to specific domains. Evaluation techniques for explainability are discussed, with attention to metrics, benchmarks, and human-centered assessment methods that ensure AI explanations are both accurate and clinically relevant. Ethical considerations, such as fairness, accountability, and privacy, are discussed. We highlight the importance of balancing transparency with patient confidentiality. The book provides case studies and empirical evidence illustrating the benefits and challenges of implementing XAI in real clinical settings.
Foundations of LLMs in healthcare.- Role of explainable AI.-
Explainability and Reliability of Large Language Models in Health Systems.-
Core Techniques for Explaining LLMs in Healthcare.- Designing Trustworthy and
Explainable Clinical Decision Support Systems.- Transfer Learning for
Explainable AI in Clinical LLMs.- Explainable NLP in Healthcare: Enhancing
Clinical Documentation and Information Extraction.- Case Studies of
Explainable LLMs in Diagnosis, Treatment Planning, and Patient Interaction.-
Reinforcement Learning in Healthcare: From Treatment Optimization to the
Challenge of Explainability with Large Language Models.- Evaluating
Explainability: Metrics, Benchmarks, and Human-Centered Evaluation Methods.-
Legal and Regulatory Considerations.- Future Directions Human-AI
Collaboration, Adaptive Explanations, and Regulatory Readiness.
Azadeh Zamanifar is currently head of AI/ software department and an assistant professor of Islamic Azad university, science and research branch. Her research interests include IoT based health care systems, machine learning, deep learning, and distributed systems. She received B.SC in Tehran university in 2002. She received her M.SC from Iran university of science and technology in 2008. She received her Ph.D. from Shahid Beheshti university in December 2016.
AMIR TAHERKORDI (Member, IEEE) is a Full Professor at the Department of Informatics, University of Oslo (UiO). He received his Ph.D. degree from the Informatics Department, UiO in 2011. After completing his Ph.D. studies, Amir joined Sonitor Technologies as a Senior Embedded Software Engineer. From 2013 to 2018, he was a researcher in the Networks and Distributed Systems (ND) group at the Department of Informatics, UiO. He has so far published several articles in high-ranked conferences and journals, and he has experience from several national (Norwegian Research Council) and international (European research funding agencies) research projects. He is an Associate Editor of IEEE Transactions on Mobile Computing and IEEE Transactions on Network Science and Engineering. Amirs research interests are broadly on resource-efficiency, scalability, adaptability, dependability, mobility and data-intensiveness of distributed systems designed for emerging computing technologies, such as Internet of Things (IoT), Fog/Edge/Cloud Computing, and Cyber-Physical Systems (CPS).
Amirfarhad Farhadi holds a Ph.D. in Artificial Intelligence and is currently a Postdoctoral Fellow at Iran University of Science and Technology. He also serves as an Adjunct Professor in the Department of Computer Engineering at the Science and Research Branch of Islamic Azad University. His research expertise spans Artificial Intelligence (AI), machine learning, deep learning, transfer learning, reinforcement learning, natural language processing (NLP), and healthcare systems. Dr. Farhadi serves as a reviewer for esteemed journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and IEEE Transactions on Neural Networks and Learning Systems, among others. In addition to his academic contributions, he holds patents in robotics and actively participates in the AI industry, focusing on innovative applications and technological advancements.
