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LLM and Generative AI for Healthcare: A Comprehensive Guide

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Book Details
Language
English
Publishers
Independently published (15 July 2024)
Weight
0.06 KG
Publication Date
15/07/2024
Pages
34 pages
ISBN-13
9798333169464
Dimensions
15.24 x 0.2 x 22.86 cm
SKU
9798333169464
Author Name
Vemula (Author)
Anand Vemula is a technology, business, ESG and Risk governance Evangelist. He has more than 27 plus years of experience. Has worked in MNC at a CXO level. Has been a part of various projects and forums across customers in BFSI, Healthcare, Retail, Manufacuring, Lifesciences, Energy Industry Verticals. Certified in all the technologies and Enterprise Digital ArchitectRead more about this authorRead less about this author
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"LLM and Generative AI for Healthcare: A Comprehensive Guide" explores the transformative power of Large Language Models (LLMs) and Generative AI in the healthcare industry. This book provides a deep dive into how these cutting-edge technologies are revolutionizing medical practices, enhancing patient care, and optimizing operational efficiency.

The journey begins with an introduction to LLMs and Generative AI, offering a clear understanding of their evolution, capabilities, and significance in healthcare. It delves into the fundamentals of healthcare data, emphasizing the types of data, privacy, security considerations, and regulatory compliance, which are crucial for any AI application in this sector.

In the second part, the book showcases various applications of AI in healthcare. It covers AI's role in medical imaging and diagnostics, highlighting advancements in radiology and automated image analysis through real-world case studies.

The book also explores Natural Language Processing (NLP) applications, including clinical documentation, EHR management, voice assistants, and text mining for research and drug discovery. Furthermore, it discusses personalized medicine, predictive analytics for patient outcomes, and AI's role in drug discovery and development.

The third part focuses on the implementation of AI solutions in healthcare. It provides practical guidance on designing AI systems, integrating them with existing healthcare infrastructure, and key design considerations.

The book also covers data management, preprocessing techniques, and ensuring data quality, followed by model training, evaluation, deployment strategies, and continuous improvement. Real-world case studies and lessons learned from successful AI implementations are presented in the fourth part.

This section also addresses the ethical and legal considerations of AI in healthcare, emphasizing the importance of fairness, transparency, and compliance with regulations. The book concludes with a look at future trends and innovations in AI, preparing readers for upcoming technological advancements.

The final part offers hands-on tutorials and exercises, guiding readers through the setup and use of popular AI tools and libraries. It includes basic and advanced projects, such as building medical chatbots and diagnostic tools, to reinforce learning and practical application.

"LLM and Generative AI for Healthcare: A Comprehensive Guide" is an essential resource for healthcare professionals, data scientists, and AI enthusiasts looking to harness the power of AI to improve healthcare outcomes. .

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