Integrating Generative Ai In Medical Practice
Download Integrating Generative Ai In Medical Practice PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Integrating Generative Ai In Medical Practice book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Integrating Generative AI in Education to Achieve Sustainable Development Goals
A new challenge has become present in the field of generative artificial intelligence (AI). The fundamental nature of education, a vital element for advancing the United Nations' Sustainable Development Goals (SDGs), now grapples with the transformative impact of AI technologies. As we stand at this intersection of progress and pedagogy, critical questions surface about the future roles of educators and the integrity of assessment processes. AI’s rapid progression prompts an exploration of the competencies our education systems must cultivate in a world where human and machine intelligence are becoming increasingly interconnected. Against this backdrop of transformative uncertainty, Integrating Generative AI in Education to Achieve Sustainable Development Goals addresses profound challenges and offers promising solutions at the crossroads of AI and education. This book assembles distinguished academics, researchers, and practitioners, forming a collective voice on the intersection of Generative AI and education. The three-part structure dissects the technical aspects of AI-powered innovations in educational design, explores multidisciplinary applications enhancing educational content, and highlights AI-driven solutions to address equality and inclusion concerns within educational systems. The book also underscores the importance of ethical considerations of generative AI to ensure a future where technology serves the broader goals of sustainability and equitable education.
Artificial Intelligence in Healthcare
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Application of Generative AI in Healthcare Systems
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.