Intelligent systems in medicine and health : the role of AI / Trevor A. Cohen, Vimla L. Patel, Edward H. Shortliffe, editors.

Format:

Book

Contributor:

Cohen, Trevor, editor.

Patel, Vimla L., editor.

Shortliffe, Edward Hance, editor.

Series:

Cognitive informatics in biomedicine and healthcare

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Medical informatics.

Genre:

Electronic books.

Physical Description:

1 online resource (xxv, 598 pages) : illustrations (some color).

Place of Publication:

Cham : Springer, [2022]

Summary:

This textbook comprehensively covers the latest state-of-the-art methods and applications of artificial intelligence (AI) in medicine, placing these developments into a historical context. Factors that assist or hinder a particular technique to improve patient care from a cognitive informatics perspective are identified and relevant methods and clinical applications in areas including translational bioinformatics and precision medicine are discussed. This approach enables the reader to attain an accurate understanding of the strengths and limitations of these emerging technologies and how they relate to the approaches and systems that preceded them. With topics covered including knowledge-based systems, clinical cognition, machine learning and natural language processing, Intelligent Systems in Medicine and Health: The Role of AI details a range of the latest AI tools and technologies within medicine. Suggested additional readings and review questions reinforce the key points covered and ensure readers can further develop their knowledge. This makes it an indispensable resource for all those seeking up-to-date information on the topic of AI in medicine, and one that provides a sound basis for the development of graduate and undergraduate course materials.

Contents:

Intro

Foreword

Preface

The State of AI in Medicine

Introducing Intelligent Systems in Medicine and Health: The Role of AI

Structure and Content

Guide to Use of This Book

Acknowledgments

Contents

Contributors

Part I: Introduction

Chapter 1: Introducing AI in Medicine

The Rise of AIM

Knowledge-Based Systems

Neural Networks and Deep Learning

Machine Learning and Medical Practice

The Scope of AIM

From Accurate Predictions to Clinically Useful AIM

The Cognitive Informatics Perspective

Why CI?

The Complementarity of Human and Machine Intelligence

Mediating Safe and Effective Human Use of AI-Based Tools

Concluding Remarks

References

Chapter 2: AI in Medicine: Some Pertinent History

Introduction

Artificial Intelligence: The Early Years

Modern History of AI

AI Meets Medicine and Biology: The 1960s and 1970s

Emergence of AIM Research at Stanford University

Three Influential AIM Research Projects from the 1970s

INTERNIST-1/QMR

CASNET

MYCIN

Cognitive Science and AIM

Reflecting on the 1970s

Evolution of AIM During the 1980s and 1990s

AI Spring and Summer Give Way to AI Winter

AIM Deals with the Tumult of the 80s and 90s

The Last 20 Years: Both AI and AIM Come of Age

Chapter 3: Data and Computation: A Contemporary Landscape

Understanding the World Through Data and Computation

Types of Data Relevant to Biomedicine

Knowing Through Computation

Motivational Example

Computational Landscape

Knowledge Representation

Machine Learning

Data Integration to Better Understand Medicine: Multimodal, Multi-Scale Models

Distributed/Networked Computing

Data Federation Models

Interoperability

Computational Aspects of Privacy

Trends and Future Challenges

Ground Truth

Open Science and Mechanisms for Open data

Data as a Public Good

Part II: Approaches

Chapter 4: Knowledge-Based Systems in Medicine

What Is a Knowledge-Based System?

How Is Knowledge Represented in a Computer?

Rules: Inference Steps

Patterns: Matching

Probabilistic Models

Naive Bayes

Bayesian Networks

Decision Analysis and Influence Diagrams

Causal Mechanisms: How Things Work

How Is Knowledge Acquired?

Ontologies and Their Tools

Knowledge in the Era of Machine Learning

Incorporating Knowledge into Machine Learning Models

Graph-Based Models

Graph Representation Learning

Biomedical Applications of Graph Machine Learning

Text-Based Models

Leveraging Expert Systems to Train Models

Looking Forward

Chapter 5: Clinical Cognition and AI: From Emulation to Symbiosis

Augmenting Human Expertise: Motivating Examples

Cognitive Science and Clinical Cognition

Symbolic Representations of Clinical Information

Clinical Text Understanding

Notes:

Includes index.

Online resource; title from PDF title page (SpringerLink, viewed November 22, 2022).

ISBN:

9783031091087

3031091086

OCLC:

1350669287

Access Restriction:

Restricted for use by site license.