Artificial Intelligence in Radiology, An Issue of Radiologic Clinics of North America

1st Edition, Volume 59-6 - October 27, 2021
Editor: Daniel L. Rubin
Language: English
Hardback ISBN:
9 7 8 - 0 - 3 2 3 - 8 1 3 5 5 - 6
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 1 3 5 6 - 3

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Cover image
Title page
Table of Contents
Copyright
Contributors
CME Accreditation Page
Forthcoming Issues
Preface
Basic Artificial Intelligence Techniques: Natural Language Processing of Radiology Reports
Key points
Introduction
Natural language processing paradigms
Types of natural language processing tasks
Unique considerations for radiology report natural language processing
Use cases of natural language processing in radiology
Framework for performing radiology natural language processing
Summary
Clinics care points
Disclosure
Basic Artificial Intelligence Techniques: Machine Learning and Deep Learning
Key points
Medical images
How images are stored
Elements of machine learning
Deep learning
Summary
Basic Artificial Intelligence Techniques: Evaluation of Artificial Intelligence Performance
Key points
The expanding use cases of artificial intelligence in health care
Generalizability of artificial intelligence algorithms
The issue of bias in machine learning
Tasks in medical image analysis
Task-specific metrics for evaluation of artificial intelligence models
Quality of ground truth
User studies
Interpretability
Summary
Disclosure
Optimization of Radiology Workflow with Artificial Intelligence
Key points
Introduction
Order entry support
Scheduling and resource allocation
Operational efficiency
Scan protocolling
Worklist optimization
Hanging protocols
Report generation
Final considerations
Clinics care points
Disclosure statement
Upstream Machine Learning in Radiology
Key points
Introduction
Discussion
Summary
Clinics care points
Disclosure
Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen
Key points
Introduction
Chest imaging applications
Abdominal imaging applications
Challenges of artificial intelligence application and future works
Clinics care points
Clinical Artificial Intelligence Applications in Radiology: Neuro
Key points
Introduction
Neuroradiology examples
Machine Learning Competitions
Centers for Medicare and Medicaid Services approval
Reviews
Clinics care points
Summary
Disclosure
Clinical Artificial Intelligence Applications: Musculoskeletal
Key points
Introduction
General tasks
Change detection and prediction
Task assistance
Technical assistance
Outcome prediction and risk stratification
Summary
Clinical Artificial Intelligence Applications: Breast Imaging
Key points
Introduction
Human-engineered analytics and deep learning techniques
Artificial intelligence in breast cancer risk assessment and prevention
Artificial intelligence in breast cancer screening and detection
Artificial intelligence in breast cancer diagnosis and prognosis
Artificial intelligence in breast cancer treatment response and risk of recurrence
Challenges in the field and future directions
Clinics care points
Funding information
Artificial Intelligence Enabling Radiology Reporting
Key points
Introduction
Challenges and opportunities in artificial intelligence for report preparation, assembly, and clinical integration
Summary
Clinics care points
Disclosure
Artificial Intelligence for Quality Improvement in Radiology
Key points
Introduction
Operational quality improvement
Report quality improvement
Expanding our reach
Summary
Clinics care points
Disclosure
Separating Hope from Hype: Artificial Intelligence Pitfalls and Challenges in Radiology
Key points
How can artificial intelligence provide value to radiologists?
What challenges must be overcome for artificial intelligence to provide value to radiologists?
How can these challenges be overcome?
Clinics care points
Regulatory Issues and Challenges to Artificial Intelligence Adoption
Key points
Introduction
Surveying the regulatory landscape: An alphabet soup
Implementation challenges: The growing pains of artificial intelligence expansion
Clinics care points
Summary
Disclosure
Future Directions in Artificial Intelligence
Key points
Introduction
Future regulatory environment
The patient as a direct consumer of medical imaging artificial intelligence
Architecture of the future in the delivery of artificial intelligence algorithms
Data for artificial intelligence in the future
Development of new radiology specific neural network architectures (beyond ImageNET)
Non–pixel-based artificial intelligence applications will be developed in parallel to pixel-based ones
Artificial intelligence for the assessment and improvement of image quality
Artificial intelligence as a scribe or personal assistant
Artificial intelligence for image acquisition
Future artificial intelligence will take previous studies into account
Disease-based multidisciplinary clinical packages
Autonomous artificial intelligence
Next-generation technologies in artificial intelligence
Beyond deep learning and artificial intelligence
Summary
Clinics care points
Disclosure

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Daniel L. Rubin