LLMs and Generative AI for Healthcare

LLMs and Generative AI for Healthcare

by Kerrie Holley, Manish Mathur
Released August 2024
Publisher(s): O'Reilly Media, Inc.
ISBN: 9781098160920

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Book description

Large language models (LLMs) and generative AI are rapidly changing the healthcare industry. These technologies have the potential to revolutionize healthcare by improving the efficiency, accuracy, and personalization of care. This practical book shows healthcare leaders, researchers, data scientists, and AI engineers the potential of LLMs and generative AI today and in the future, using storytelling and illustrative use cases in healthcare.

Authors Kerrie Holley, former Google healthcare professionals, guide you through the transformative potential of large language models (LLMs) and generative AI in healthcare. From personalized patient care and clinical decision support to drug discovery and public health applications, this comprehensive exploration covers real-world uses and future possibilities of LLMs and generative AI in healthcare.

With this book, you will:

  • Understand the promise and challenges of LLMs in healthcare
  • Learn the inner workings of LLMs and generative AI
  • Explore automation of healthcare use cases for improved operations and patient care using LLMs
  • Dive into patient experiences and clinical decision-making using generative AI
  • Review future applications in pharmaceutical R&D, public health, and genomics
  • Understand ethical considerations and responsible development of LLMs in healthcare

"The authors illustrate generative's impact on drug development, presenting real-world examples of its ability to accelerate processes and improve outcomes across the pharmaceutical industry." --Harsh Pandey, VP, Data Analytics & Business Insights, Medidata-Dassault

Kerrie Holley is a retired Google tech executive, IBM Fellow, and VP/CTO at Cisco. Holley's extensive experience includes serving as the first Technology Fellow at United Health Group (UHG), Optum, where he focused on advancing and applying AI, deep learning, and natural language processing in healthcare.

Manish Mathur brings over two decades of expertise at the crossroads of healthcare and technology. A former executive at Google and Johnson & Johnson, he now serves as an independent consultant and advisor. He guides payers, providers, and life sciences companies in crafting cutting-edge healthcare solutions.

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Table of contents

  1. Introduction
    1. Why We Wrote This Book
    2. Who This Book Is For
    3. How This Book Is Organized
      1. Chapter 1: Doctor’s Black Bag
      2. Chapter 2: Peeking Inside the AI Black Box
      3. Chapter 3: Beyond White Coats
      4. Chapter 4: LLM and Generative AI’s Patient and Clinical Potential
      5. Chapter 5: LLMs in Pharmaceutical R&D, Public Health, and Beyond
      6. Chapter 6: Steering the Helm for Ethical Use of LLMs
      7. Chapter 7: Objects Are Closer Than They Appear
    4. O’Reilly Online Learning
    5. How to Contact Us
    6. Acknowledgments
      1. Kerrie Holley
      2. Manish Mathur
      3. Kerrie and Manish
  2. 1. Doctor’s Black Bag
    1. Potential of LLMs and Generative AI
    2. Promise and Possibilities of LLMs in Healthcare
      1. Medical Swiss Army Knife App for Consumers
      2. Medical Sherpa App for Clinicians
    3. LLMs’ Emerging Features
      1. Infinite Context Prompts
      2. Agentic Reasoning
    4. Context of Use When Using LLMs
    5. Consumer and Business LLMs
      1. Consumer LLMs and Generative AI
      2. Business LLMs and Generative AI
      3. Bridging the Divide
    6. Summary
  3. 2. Peeking Inside the AI Black Box
    1. LLMs and Generative AI
    2. AI and Machine Learning
      1. Detecting a Tumor with Deep Learning
      2. Natural Language Processing and Computer Vision
    3. The Anatomy of an LLM
      1. Transformers
      2. Tokens
      3. Attention
      4. Parameters
    4. LLM and Generative AI Potential
    5. Art of Building LLMs
      1. Retrieval Augmented Generation
      2. Conceptualization
      3. Data Selection and Curation
      4. Model Architecture and Design
      5. Prompt Engineering
      6. Refinement and Feedback
      7. Integration with Apps
    6. Summary
  4. 3. Beyond White Coats
    1. Current State of Automation in Healthcare
      1. The Promise of LLMs in Healthcare
      2. Historical Use of Machine Learning
    2. Using LLMs and Generative AI for Healthcare Automation
      1. Unlocking Medical Secrets: How LLMs Revolutionize Drug Discovery
    3. Automating and Managing Healthcare
      1. AI Healthcare Assistant
      2. Automating Administrative Tasks from a Healthcare System Perspective
      3. Coding and Billing
      4. Clinical Processes
      5. Optimizing Healthcare Workflows
      6. Transforming the Clinical Trial Landscape
    4. Summary
  5. 4. LLM and Generative AI’s Patient and Clinical Potential
    1. Patient Experience
      1. Health Bot Concierge
      2. Doctor’s Notes and Visits
      3. Health Plan Wizard
      4. Black Maternal Health
      5. Medication Reminder
      6. Oral Health
      7. Symptom Checker
      8. Clinical Decision Support
      9. Clinical Insight Bot
      10. AI Curbside Physician
      11. Remote Patient Monitoring
      12. Digital Twin
      13. Doctor Letter Generation
      14. Health Equity
      15. Prior Authorization
    2. Summary
  6. 5. LLMs in Pharmaceutical R&D, Public Health, and Beyond
    1. Pharma Research and Development
      1. Drug Discovery
      2. Literature Review
      3. Clinical Trial Recruitment
      4. Pharmaceutical Commercial
    2. Public Health
      1. Disease Surveillance
      2. Health Education and Promotion
      3. Mental Health
      4. Disaster Preparedness and Response
    3. Genomics
    4. Summary
  7. 6. Steering the Helm for Ethical Use of LLMs
    1. AI as a Force for Good: Improving Healthcare
    2. Ethical Implications of LLMs
      1. Fabricated Reality
      2. Impersonation and Fraud
      3. Deep Fakes
      4. Personalized Persuasion
      5. Bias Amplification
      6. Insidious Influence at Scale
      7. Automated Hacking
      8. Prompt Injection Attacks
      9. Addressing Foreseeable Use Cases
    3. Monitoring LLM Behavior
    4. Security and Privacy
      1. Federated Learning
      2. Differential Privacy
      3. Prompt Sanitization and Filtering
      4. Homomorphic Encryption
      5. Explainable AI
    5. AI and the Paper Clip Problem
    6. Policy Development
    7. Summary
  8. 7. Objects Are Closer Than They Appear
    1. Future Prospects and Challenges in LLMs
      1. Infinite Prompts
      2. Agentic Reasoning
    2. AGI
    3. Whispers of Tomorrow: Five Predictions
      1. AI Calls the Shots
      2. Medi-Sphere Personalized Orb
      3. Health Avatar: Personal AI Health Assistant
      4. Virtual Nurse Avatar
      5. The Rise of AI/LLM-Driven Applications
    4. Summary
  9. Index
  10. About the Authors

Product information

  • Title: LLMs and Generative AI for Healthcare
  • Author(s): Kerrie Holley, Manish Mathur
  • Release date: August 2024
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098160920