Hazrat Ali – författare

This book offers a timely introduction to the rapidly evolving role of artificial intelligence in modern healthcare. Designed for a broad audience, including students, practitioners, researchers, and librarians, it provides clear explanations and a solid conceptual foundation for understanding current advances and anticipating future breakthroughs in healthcare AI. A central focus of the book is the transformative rise of large language models (LLMs). These models now influence clinical documentation, decision support, knowledge extraction, and patient communication. While showcasing their potential to enhance healthcare delivery, the book also examines the concerns they raise, including ethical risks, transparency, fairness, and the significant resources required to deploy them safely. Bringing together contributions from international experts, the book offers a comprehensive overview of how LLMs are being applied across medical imaging, diagnostics, multimodal data analysis, and clinical workflows. It also highlights the practical challenges of fine-tuning and integrating these systems into real-world clinical environments.Importantly, the book provides thought-provoking insights on trust, accountability, and responsible AI, making it an essential reference for anyone seeking to navigate the opportunities and challenges of LLM-driven healthcare.Key Features:* A timely comprehensive coverage of LLMs in healthcare.* Contributions from a global network of authors from ten different countries.* Insightful discussions on ethics, trust, and hallucinations in LLMs.* Real world case studies and fills the gap between technical AI research and implementation.* Value for a broad audience, serving as an essential resource for researchers, clinicians, policymakers, and advanced students.Dr. Hazrat Ali is a Lecturer in Artificial Intelligence at the University of Stirling, United Kingdom. He is a senior member of the IEEE and Associate Editor at Wiley Applied, Nature Scientific Reports journals, and leads multiple research initiatives in Healthcare AI. Mubashir Husain Rehmani (M'14-SM'15, SFHEA) received the Ph.D. degree from the University Pierre and Marie Curie, Paris, in 2011. He is currently working as Lecturer in the Department of Computer Science, Munster Technological University, Ireland. He is the recipient of Highly Cited Researcher award four times in 2020, 2021, 2022, and 2025 by Clarivate, USA.

This book offers a timely introduction to the rapidly evolving role of artificial intelligence in modern healthcare. Designed for a broad audience, including students, practitioners, researchers, and librarians, it provides clear explanations and a solid conceptual foundation for understanding current advances and anticipating future breakthroughs in healthcare AI. A central focus of the book is the transformative rise of large language models (LLMs). These models now influence clinical documentation, decision support, knowledge extraction, and patient communication. While showcasing their potential to enhance healthcare delivery, the book also examines the concerns they raise, including ethical risks, transparency, fairness, and the significant resources required to deploy them safely. Bringing together contributions from international experts, the book offers a comprehensive overview of how LLMs are being applied across medical imaging, diagnostics, multimodal data analysis, and clinical workflows. It also highlights the practical challenges of fine-tuning and integrating these systems into real-world clinical environments.Importantly, the book provides thought-provoking insights on trust, accountability, and responsible AI, making it an essential reference for anyone seeking to navigate the opportunities and challenges of LLM-driven healthcare.Key Features:* A timely comprehensive coverage of LLMs in healthcare.* Contributions from a global network of authors from ten different countries.* Insightful discussions on ethics, trust, and hallucinations in LLMs.* Real world case studies and fills the gap between technical AI research and implementation.* Value for a broad audience, serving as an essential resource for researchers, clinicians, policymakers, and advanced students.Dr. Hazrat Ali is a Lecturer in Artificial Intelligence at the University of Stirling, United Kingdom. He is a senior member of the IEEE and Associate Editor at Wiley Applied, Nature Scientific Reports journals, and leads multiple research initiatives in Healthcare AI. Mubashir Husain Rehmani (M'14-SM'15, SFHEA) received the Ph.D. degree from the University Pierre and Marie Curie, Paris, in 2011. He is currently working as Lecturer in the Department of Computer Science, Munster Technological University, Ireland. He is the recipient of Highly Cited Researcher award four times in 2020, 2021, 2022, and 2025 by Clarivate, USA.

Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.

Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence.

This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models.

This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.