Description: The integration of medical large language models (LLMs) and AI in healthcare transforms how medical professionals diagnose conditions, manage patient care, and access clinical information. These advanced technologies may improve efficiency, enhance decision-making, and expand access to healthcare services through applications like virtual assistants, medical documentation, predictive analytics, and personalized treatment recommendations. However, the increasing use of AI in clinical settings also raises important concerns related to ethics, patient privacy, bias, accountability, and trust between healthcare providers and patients. Further examination may reveal the opportunities and challenges associated with AI-driven systems in medicine, emphasizing the need for responsible, transparent, and human-centered approaches to healthcare innovation. Medical LLMs and AI in Healthcare: Ethics, Trust, and Clinical Applications explores the application of medical LLMs and AI in healthcare to support clinical decision-making, patient care, medical documentation, and healthcare accessibility. It examines the ethical, legal, and social implications of AI in medicine, including issues of trust, bias, privacy, accountability, and the relationship between technology, healthcare professionals, and patients. This book covers topics such as medical technologies, radiology, and data science, and is a useful resource medical and healthcare professionals, engineers, academicians, researchers, and scientists.
