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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
Purchase options
Institutional subscription on ScienceDirect
Request a sales quote- 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
- No. of pages: 240
- Language: English
- Edition: 1
- Volume: 59-6
- Published: October 27, 2021
- Imprint: Elsevier
- Hardback ISBN: 9780323813556
- eBook ISBN: 9780323813563
DR
Daniel L. Rubin
Affiliations and expertise
Professor of Biomedical Data Science, Radiology, and Medicine
Department of Biomedical Data Science
Stanford University
Medical School Office Building (MSOB)
Room X-335, MC 5464
1265 Welch Road
Stanford, CA 94305-5479