Azadeh Zamanifar – författare

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.

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.

This book offers a comprehensive exploration into the role of Large Language Models (LLMs) in modern healthcare. It focuses specifically on the lifecycle of LLM deployment in healthcare settings, including transparency, accountability, data privacy, and regulatory compliance to ensure safe and effective use.

Generative AI has immensely influenced various fields, such as education, marketing, art and music, and especially healthcare. Generative AI can benefit the patient through various approaches. For instance, it can enhance the image qualities negatively affected by radiation reduction, preventing patients from needing to repeat the image-taking process. Also, the generation of one type of image from another more expensive one can help patients save funds. Generative AI facilitates the administrative process, letting the doctor focus more on the treatment process. It even goes further by helping medical professionals with diagnosis and decision- making, suggesting possible treatment plans according to the patient symptoms.

This book introduces several practical GenAI healthcare applications, especially in medical imaging, pandemic prediction, synthetic data generation, clinical administration support, professional education, patient engagement, and clinical decision support, providing a review of efficient GenAI tools and frameworks in this area. GenAI empowers the treatment process through several methods; however, some ethical, privacy, and security challenges require attention. Despite the challenges presented, GenAI technological and inherited characteristics smooth the path of improvement for it in the future.