Explainable Large Language Models in Healthcare Applications
暫譯: 可解釋的大型語言模型在醫療應用中的應用

Zamanifar, Azadeh, Taherkordi, Amir, Farhadi, Amirfarhad

Explainable Large Language Models in Healthcare Applications
  • 出版商: Springer
  • 出版日期: 2026-05-20
  • 售價: $7,970
  • 貴賓價: 9.8 折 $7,810
  • 語言: 英文
  • 頁數: 325
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3032150876
  • ISBN-13: 9783032150875
  • 相關分類: Large language model

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

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.

商品描述(中文翻譯)

這是一本全面探討可解釋人工智慧（XAI），特別是大型語言模型（LLMs）如何改變醫療保健的書籍。該書涵蓋了XAI的基礎概念，強調在以AI驅動的醫療系統中，透明度、問責性和可解釋性對於臨床醫生和病人信任的重要性。它檢視了可解釋AI的原則和方法論，詳細說明了LLMs如何通過解釋、模型設計和以人為中心的描述，使複雜的機器學習輸出變得易於理解。

書中有一部分專門介紹實際應用，例如疾病診斷、治療計劃和病人管理。它展示了XAI如何改善臨床決策和病人結果。書中討論了可解釋的LLMs如何整合進電子健康紀錄（EHRs）和臨床工作流程。它顯示這些技術如何促進數據分析、改善文檔記錄並支持護理。該書還探討了在醫療保健中部署可解釋LLMs的挑戰和限制，包括隱私問題、數據複雜性以及將模型適應特定領域的問題。書中討論了可解釋性的評估技術，重點關注確保AI解釋既準確又臨床相關的指標、基準和以人為中心的評估方法。倫理考量，如公平性、問責性和隱私問題，也被討論。我們強調在透明度與病人保密之間取得平衡的重要性。該書提供了案例研究和實證證據，說明在實際臨床環境中實施XAI的好處和挑戰。

作者簡介

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. Amir's 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.

作者簡介(中文翻譯)

Azadeh Zamanifar 目前是伊斯蘭阿茲哈大學（Islamic Azad University）科學與研究分校的人工智慧/軟體部門負責人及助理教授。她的研究興趣包括基於物聯網的健康照護系統、機器學習、深度學習和分散式系統。她於 2002 年在德黑蘭大學獲得學士學位，並於 2008 年在伊朗科技大學獲得碩士學位。她於 2016 年 12 月在沙希德·貝赫什提大學（Shahid Beheshti University）獲得博士學位。

AMIR TAHERKORDI（IEEE 會員）是奧斯陸大學（University of Oslo, UiO）資訊學系的全職教授。他於 2011 年在奧斯陸大學資訊學系獲得博士學位。完成博士學業後，Amir 加入 Sonitor Technologies 擔任高級嵌入式軟體工程師。從 2013 年到 2018 年，他在奧斯陸大學資訊學系的網路與分散式系統（ND）小組擔任研究員。他迄今已在多個高排名的會議和期刊上發表了數篇文章，並參與了多個國內（挪威研究委員會）和國際（歐洲研究資助機構）的研究項目。他是《IEEE 行動計算期刊》（IEEE Transactions on Mobile Computing）和《IEEE 網路科學與工程期刊》（IEEE Transactions on Network Science and Engineering）的副編輯。Amir 的研究興趣廣泛涵蓋資源效率、可擴展性、適應性、可靠性、流動性和針對新興計算技術（如物聯網（IoT）、霧計算/邊緣計算/雲計算和網路物理系統（CPS））設計的分散式系統的數據密集性。

Amirfarhad Farhadi 擁有人工智慧（AI）博士學位，目前是伊朗科技大學的博士後研究員。他同時在伊斯蘭阿茲哈大學科學與研究分校的計算機工程系擔任兼任教授。他的研究專長涵蓋人工智慧、機器學習、深度學習、遷移學習、強化學習、自然語言處理（NLP）和健康照護系統。Farhadi 博士擔任多本知名期刊的審稿人，包括《IEEE 圖案分析與機器智慧期刊》（IEEE Transactions on Pattern Analysis and Machine Intelligence, TPAMI）和《IEEE 神經網路與學習系統期刊》（IEEE Transactions on Neural Networks and Learning Systems）等。除了學術貢獻外，他在機器人技術方面擁有專利，並積極參與人工智慧產業，專注於創新應用和技術進步。

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