Full Description
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.
Contents
.- 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.
