Generative Ai In Clinical Practice.

Format:

Book

Author/Creator:

Quinn, Campion

Contributor:

World Scientific Pub. Co.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (343 p.)

Place of Publication:

Singapore : World Scientific Publishing Company, 2025.

Contents:

Intro

Contents

About the Author

Introduction: From Promise to Practice

Chapter 1 Generative AI Arrives in Clinical Medicine

Introduction

Learning Goals for Chapter 1

Prologue-A Tale of Two Encounters

1.1 The Long Arc of Medical Computation

1.1.1 From Punch Cards to Automated Blood Counts (1950s-1970s)

1.1.2 From Rule-Based Expert Systems to Probabilistic Models (1980s)

1.1.3 Machine Learning's Imaging Breakthrough (2012-2018)

1.2 How Generative Models Create Language and Images

1.2.1 Autoregressive Language Modeling in Plain English

1.2.2 Diffusion Models and Synthetic Imaging

1.2.3 Why Generation Differs from Classification

1.3 Hardware, Data, and Open-Source Catalysts

1.3.1 Falling GPU Cost Curves

1.3.2 Public Datasets and the Permissive-License Boom

1.3.3 Corporate and Academic Consortia: Coordination at Scale

1.4 Pain Points at the Bedside

1.4.1 Note Bloat and After-Hours Charting

1.4.2 Prior Authorization Delays

1.4.3 Diagnostic Uncertainty in Rare Disease

1.5 Preview of Use Cases Covered in This Book

1.5.1 Triage, Dictation, and Decision Support Front-Door Triage

1.5.2 Patient Engagement, Coaching, and Summarization

1.5.3 Research Synthesis and Administrative Relief

References

Chapter 2 Data and Algorithms-From Raw Inputs to Actionable Results

Learning Goals for Chapter 2

2.1 Types of Clinical Data

2.2 Preprocessing Health Care Data

2.3 Case Vignette 2.1: Sensor Data in Heart Failure Monitoring

2.4 Training AI Models: From Data to Algorithm

2.5 Model Validation and Evaluation

2.6 Trust Through Provenance and Quality Control

2.7 Regulatory Expectations: The FDA Life Cycle Approach

2.8 Conclusion

3.4 Prompting for Patient Communication and Health Literacy

3.5 Iterative Prompting and Prompt Chaining

3.6 Prompt Safety and Ethical Use in Clinical Practice

3.6.1 Prompt Optimization and Iteration

3.6.2 Prompt Toolkit for Routine Clinical Tasks

3.7 Putting It All Together-Prompt Engineering in Real Clinical Workflows

3.8 Conclusion

Chapter 4 Integrating AI into the Clinical Workflow

Learning Goals for Chapter 4

4.1 Mapping the Clinical Workflow

4.1.1 Pre-Visit Intake and Triage

4.1.2 Clinical Encounter and Documentation

Notes:

References -- Check Your Understanding: Chapter 2 Quiz -- Instructions for the Reader -- Multiple Choice (Choose the best answer) -- Answer Key -- Chapter 3 Prompt Craft for Conversational Artificial Intelligence -- Introduction -- Learning Goals for Chapter 3 -- 3.1 Understanding the Anatomy of a Prompt -- 3.2 Prompt Engineering: Why Structure Matters -- 3.2.1 Role Assignment and Context Setting -- 3.2.2 Prompting for Clinical Documentation -- 3.2.3 Common Documentation Tasks for AI -- 3.3 Prompt Engineering for Clinical Communication and Decision Support

4.1.3 Post-Visit Follow-Up

Electronic reproduction. Singapore Available via World Wide Web.

Other Format:

Print version: Quinn, Campion Generative Ai In Clinical Practice: A Physician's Guide To Transforming Medicine

ISBN:

9789819822218

9819822211

Publisher Number:

90103411422

Access Restriction:

Restricted for use by site license.