Amir Taherkordi – författare

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5 produkter

AI Safety in Healthcare Systems
Resilience, Transparency, and Accountability
AvAmir Taherkordi,Amirfarhad Farhadim. fl.
E-bok
PDF, Engelska, 2026
1 047 kr
Läs direkt efter köp
This book provides an in-depth exploration of AI safety in healthcare. It bridges the gap between technical innovation and patient protection. It examines emerging methodologies for risk management, transparency, and robust AI system design used specifically in medical applications.
Readers will gain insights into the ethical considerations that surround AI deployment, techniques for improving the explainability and accountability of AI models, and the implementation of safety protocols to detect and prevent AI failures. The book also discusses the evolving regulatory view that shapes AI adoption and how privacy-preserving technologies can enable safer data sharing across institutions. In highlighting both current challenges and future directions, the book makes a meaningful contribution to the safe, responsible, and trustworthy integration of AI in healthcare systems. This is an illuminating read for researchers, practitioners, policymakers, and healthcare providers interested in acquiring the knowledge needed to harness AIâ€™s potential while safeguarding human health and well-being.
AI Safety in Healthcare Systems
Resilience, Transparency, and Accountability
AvAmir Taherkordi,Amirfarhad Farhadim. fl.
E-bok
Engelska, 2026
1 047 kr
Läs direkt efter köp
This book provides an in-depth exploration of AI safety in healthcare. It bridges the gap between technical innovation and patient protection. It examines emerging methodologies for risk management, transparency, and robust AI system design used specifically in medical applications.
Readers will gain insights into the ethical considerations that surround AI deployment, techniques for improving the explainability and accountability of AI models, and the implementation of safety protocols to detect and prevent AI failures. The book also discusses the evolving regulatory view that shapes AI adoption and how privacy-preserving technologies can enable safer data sharing across institutions. In highlighting both current challenges and future directions, the book makes a meaningful contribution to the safe, responsible, and trustworthy integration of AI in healthcare systems. This is an illuminating read for researchers, practitioners, policymakers, and healthcare providers interested in acquiring the knowledge needed to harness AIâ€™s potential while safeguarding human health and well-being.
AI Safety in Healthcare Systems
Resilience, Transparency, and Accountability
AvAmir Taherkordi,Azadeh Zamanifarm. fl.
Inbunden, Engelska, 2026
1 851 kr
Skickas inom 10-15 vardagar
This book provides an in-depth exploration of AI safety in healthcare. It bridges the gap between technical innovation and patient protection. It examines emerging methodologies for risk management, transparency, and robust AI system design used specifically in medical applications. Readers will gain insights into the ethical considerations that surround AI deployment, techniques for improving the explainability and accountability of AI models, and the implementation of safety protocols to detect and prevent AI failures. The book also discusses the evolving regulatory view that shapes AI adoption and how privacy-preserving technologies can enable safer data sharing across institutions. In highlighting both current challenges and future directions, the book makes a meaningful contribution to the safe, responsible, and trustworthy integration of AI in healthcare systems. This is an illuminating read for researchers, practitioners, policymakers, and healthcare providers interested in acquiring the knowledge needed to harness AI’s potential while safeguarding human health and well-being.
Explainable Large Language Models in Healthcare Applications
AvAzadeh Zamanifar,Amir Taherkordim. fl.
Inbunden, Engelska, 2026
1 928 kr
Skickas inom 10-15 vardagar
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.
Explainable Large Language Models in Healthcare Applications
AvAmirfarhad Farhadi,Amir Taherkordim. fl.
E-bok
Engelska, 2026
2 384 kr
Läs direkt efter köp
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.

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Amir Taherkordi – författare

AI Safety in Healthcare Systems

AI Safety in Healthcare Systems

AI Safety in Healthcare Systems

Explainable Large Language Models in Healthcare Applications

Explainable Large Language Models in Healthcare Applications

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