Название: Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity
Автор: Sakshi Gupta, Umesh Gupta, Moolchand Sharma, Kamal Malik
Издательство: CRC Press
Год: 2025
Страниц: 398
Язык: английский
Формат: pdf (true), epub
Размер: 21.2 MB

This book explores the intersection of Artificial Intelligence (AI) and healthcare with a focus on generative intelligence. The book will introduce the concept of generative intelligence and its transformative potential in patient care, showcasing applications that go beyond conventional AI approaches.

Generative Intelligence in Healthcare: Transforming Patient Care with AI Creativity delves into the use of generative models for personalized medicine, data analytics, and predictive modelling, providing real-world examples of how AI creativity can revolutionize treatment strategies and diagnostic processes. It focuses on the origin and basics of Generative AI, Generative AI models, and possible areas in healthcare where Generative AI can work. It discusses how Generative AI model will help healthcare providers automatically generate prescriptions, discharge summaries, and patient conditions. The unique strength of this book lies in its comprehensive examination of ethical considerations and regulatory frameworks, ensuring a responsible and transparent integration of generative intelligence in healthcare. By addressing current challenges and envisioning future directions, this book serves as a valuable resource for healthcare professionals, researchers, and policymakers seeking to harness the full potential of AI creativity to enhance patient outcomes.

Image-to-image translation is one of the most important transformative fields, where the picture is converted into the single domain. This technique uses recent Machine Learning (ML) models to change certain visual characteristics like style, colour, or texture to complete the new image by preserving the original core meaning. Image-to-image translation is a ML and computer vision approach which converts an image from one representation to another. In this approach, Deep Learning (DL) models are applied to learn the mappings between multiple visual domains for different transformations such as black-and-white pictures, transforming sketches into realistic images, and changing the image style. Image-to-image translation is adaptable due to its real-time applications in many industries, including relevance for many image domains in health and autonomous driving applications.

The book is written for graduate students, researchers, and professionals in biomedical engineering, electrical engineering, signal process engineering, biomedical imaging, and Computer Science.