Large Language Models (LLMs) for Healthcare

A Practical Guide to Their Process and Evaluation
1. Auflage 2025
ISBN: 978-1-032-88728-9
Verlag: Taylor & Francis Ltd

Buch, Englisch, 220 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 453 g

In today’s rapidly evolving healthcare environment, one technology stands at the forefront of innovation: large language models (LLMs). Far more than a fleeting hype, LLMs represent a foundational shift in how healthcare professionals interact with and derive value from data. From simplifying clinical note-writing to supporting patient engagement and enhancing administrative processes, LLMs have the power to transform nearly every corner of the healthcare ecosystem. In Large Language Models (LLMs) for Healthcare, Jeremy Harper shines a spotlight on this transformative potential. With clarity and practicality, he explores how these advanced artificial intelligence (AI) tools can reshape clinical workflows, optimize administrative tasks, and ultimately create a more responsive, patient-centered model of care. Over the course of this book, you will discover new opportunities—learn how LLMs can reduce manual documentation burdens, provide intelligent summaries of complex patient histories, and offer real-time translations of clinical jargon; understand the fundamentals—grasp what LLMs are, how they work, and why they can handle vast amounts of clinical text more effectively than previous AI tools; examine key use cases—from automated billing support and smart note generation to patient triage and ethical telehealth consultations; address risks and realities—gain insight into challenges such as "hallucinations," inherent bias, and the critical importance of patient privacy; plan for implementation—explore strategies for prompt engineering, fine-tuning, and rigorous evaluation of LLM solutions; and envision the future – glimpse how LLMs might revolutionize healthcare through enhanced back-office operations and cutting-edge clinical decision support.

Zielgruppe

Professional Practice & Development

Autoren/Hrsg.

Harper, Jeremy

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Gesundheitswirtschaft
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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 versus Fine-Tuning
Chapter 8: In-house Development of 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
Chapter 19: Measuring the Impact of LLMs in Healthcare
Chapter 20: Looking Ahead: The Future of Healthcare with LLMs References

Über Autor(innen)

Jeremy Harper is President of Owl Health Works, a consulting firm providing quality management, health informatics, and business services for their clients. He has 20 years of healthcare industry experience including academic medical centers, community hospitals, and software vendors. As an executive, his responsibilities have included planning, implementation, and management of deployments and enterprise-enhancing initiatives. He is an authority for best practices in artificial intelligence/machine learning, business strategy, data management, transformations, turnarounds, and organization growth strategies.