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Explainable Large Language Models in Healthcare Applications
Hardcover
Series: Bio-It and AI
Medical ReferenceGeneral ComputersProbability & Statistics
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ISBN13: 9783032150875
Publisher: Springer
Published: May 20 2026
Pages: 325
Weight: 1.45
Height: 0.81 Width: 6.14 Depth: 9.21
Language: English
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.
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