Название: Smarter Healthcare Through Generative Intelligence
Автор: Vibha Jain, Aditya Gupta, Amritpal Singh, Prabal Verma, Tawseef Ayoub Shaikh
Издательство: CRC Press
Серия: Advances in Computational Collective Intelligence
Год: 2027
Страниц: 205
Язык: английский
Формат: True PDF, True EPUB
Размер: 10.1 MB

Generative Artificial Intelligence (AI) has potential to improve healthcare because it can enable smarter, more efficient solutions across diagnostics, treatment, and patient care. By analyzing vast amounts of medical data, generative AI systems can identify patterns, predict disease progression, and assist in early diagnosis with remarkable accuracy. It enhances personalized medicine by generating tailored treatment plans based on individual patient profiles, including genetic, lifestyle, and clinical data.

Smarter Healthcare Through Generative Intelligence examines the convergence of healthcare and intelligent computation and how it is reshaping the global healthcare landscape. Focusing on applications and management issues, it looks at generative models, privacy-aware distributed computing, smart sensing technologies, and policy frameworks. From data augmentation to drug discovery and from mental health assessment to intelligent patient communication, the book explores the ways Generative AI can transform healthcare.

Generative Intelligence (GI) lies at the intersection of AI, ML, and Deep Learning (DL). It is a specialized domain where systems generate novel and meaningful content by leveraging vast datasets and advanced learning models. GI is a specialized domain within AI that focuses on creating new, meaningful, original content by learning from existing data. Unlike traditional AI systems that emphasize classification or decision-making, GI is centered on creativity and innovation, capable of generating outputs such as images, text, music, and even video content. It combines the foundational principles of AI, ML, and DL to achieve these objectives.

As a broad field, AI focuses on building intelligent systems that simulate human rational abilities like reasoning, problem solving, and decision-making. GI builds on these principles but goes beyond them, as it not merely automates tasks but creates new content that has not existed before. It uses techniques like rule-based systems and data-driven approaches to achieve this level of creativity. ML, a subset of AI, provides the foundational learning mechanisms for GI. ML emphasizes recognizing patterns in data and making predictions or decisions. In the context of GI, ML models are employed to generate data, often using techniques such as Bayesian Networks and Hidden Markov Models. These models enable GI systems to learn from structured or unstructured data and synthesize outputs that align with the underlying patterns.

Highlights include:

Fog computing for smart healthcare
Leveraging generative intelligence to overcome dataset limitations in emotional well-being analysis
Ethical considerations in AI-driven smart healthcare
Designing intelligent chatbots for healthcare

Generative AI is transforming healthcare into a more proactive, precise, and patient-centered industry. Written for healthcare practitioners, researchers, policymakers, and technologists, this book is a guide on how they can design, adopt, and govern generative technologies for smarter healthcare.

Contents: