Large language models (LLMs) are machine learning models that can comprehend and generate human language text. They work by analyzing massive data sets of language. They can be used for text generation, a form of generative AI, by taking an input text and repeatedly predicting the next token or word. Some notable LLMs are OpenAI's GPT series of models, Google's PaLM and Gemini, and Meta's LLaMA family of open-source models to name a few.
This book starts with discussions over how large language models are used in healthcare and the opportunities we have to change portions of the healthcare paradigm. There are amazing opportunities to save both time as a business unit as well as improve patient experiences. The book then continues with broad-reaching descriptions of what is reasonable to expect and not expect a large language model to be able to accomplish.
The author also discusses why large language models are important and how they are going to change the industry with a detailed description of large language models. He brings a highly technical discussion of vector maths and interprets it to something that non-specialists can understand. He also explains how large language model transformers work and why we’ve seen success in image generation, language generation, and implementation.
Also provided is a discussion of the planning and strategy for implementing LLM solutions and recommendations for measuring their impact. Finally, the book discusses future trends in healthcare LLMs, emerging technologies and their potential impact in the coming years and decades.
Chapter 1: Introduction to Large Language Models for Healthcare Chapter 2: Who makes the BEST Expert Chapter 3: Understanding the Technology Behind LLMs Chapter 4: The Current State of LLMs in Healthcare Chapter 5: The Data that Feeds LLMs Chapter 6: Basic Prompt Engineering Chapter 7: Prompt Engineering vs Finetuning Chapter 8: Developing LLMs for Healthcare Applications Chapter 9: Evaluating LLM Vendors Maturity for Healthcare Chapter 10: Bias in LLMs and Its Implications for Healthcare Chapter 11: Ensuring Compliance and Ethical Use Chapter 12: LLMs in Clinical Decision Support Systems Chapter 13: Patient Engagement and LLMs Chapter 14: Training and Educating Healthcare Professionals on LLMs Chapter 15: Security and Privacy Concerns with Healthcare LLMs Chapter 16: The Role of Interdisciplinary Teams in LLM Projects Chapter 17: Implementing LLM Solutions Chapter 18: Integration with Electronic Health Records (EHR) Chapter 19: Measuring the Impact of LLMs in Healthcare Chapter 20: Looking Ahead: The Future of Healthcare with LLMs References
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