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Elsevier

The Radiology AI Handbook

  • 1st Edition - October 6, 2025
  • Latest edition
  • Editors: Adam E.M. Eltorai, James M. Hillis, Rajat Chand, Sudhen B. Desai, Katherine P. Andriole
  • Language: English

**Selected for 2026 Doody's Core Titles in Diagnostic Radiology**Artificial intelligence has the potential to transform many areas of medicine and is already a growing factor… Read more

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Description

**Selected for 2026 Doody's Core Titles in Diagnostic Radiology**

Artificial intelligence has the potential to transform many areas of medicine and is already a growing factor in the field of radiology. The Radiology AI Handbook offers the current, authoritative information you need in order to better understand AI and how to incorporate it into your daily practice. Written by clinical and computer science experts in AI, this book provides a comprehensive overview of the fundamental concepts, technology, research/development/validation, and regulatory considerations for current and emerging radiology AI applications in each subspecialty.

Key features

  • Offers an indispensable introduction to this emerging field, with expert coverage of how AI can best be used in radiology
  • Provides clear explanations of fundamental concepts in AI and machine learning; current and future applications of AI that may affect the practice of radiology; and how to develop commercially viable AI applications in radiology
  • Discusses both interpretive and non-interpretive applications, and includes multiple case studies throughout
  • Serves as both an introduction to AI in radiology for students, trainees, and professionals, as well as a how-to guide for getting started on identifying, developing, testing, and commercializing AI applications
  • An eBook version is included with purchase. The eBook allows you to access all of the text, figures, and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud. Additional digital ancillary content may publish up to 6 weeks following the publication date

Readership

Radiologists, residents, trainees, clinicians, emergency medicine physicians

Table of contents

PART I Background

1. AI in Radiology—Past and Present

2. AI in Radiology—Future

3. Technical Principles

PART II Interpretive Applications

4. Interpretive Applications of Artificial Intelligence in Breast Radiology

5. Artificial Intelligence in Cardiovascular Imaging

6. Interpretive Applications: Chest

7. Artificial Intelligence in Emergency Radiology

8. Artificial Intelligence in Gastrointestinal Imaging

9. Genitourinary

10. ArtificiaI Intelligence in Head and Neck Radiology: Current Innovations, Challenges, and Future Directions

11. Interpretive Applications: Musculoskeletal

12. Neuroradiology

13. Interpretive Applications of Artificial Intelligence in Interventional Radiology

14. Artificial Intelligence in Nuclear Radiology: Unlocking the Potential for Enhanced Patient

PART III Noninterpretive Applications

15. Patient Facing Noninterpretive Artificial Intelligence Applications

16. Navigating the Radiologic Technologist’s Landscape: Current Innovations and Future Directions of Artificial Intelligence in Radiology

17. Business-Facing Approaches

18. Noninterpretive Application of Artificial Intelligence in Radiology:
Population Health

PART IV Develop Your Application

19. Data Curation

20. Artificial Intelligence Network Training and Validation in Radiology: Recent Developments and Real-World Examples

21. Regulatory Considerations for Radiology Artificial Intelligence/Machine Learning Devices

PART V Case Studies

22. Response to COVID With Artificial Intelligence—Assisted Radiologic Diagnosis

23. Arterys Artificial Intelligence: Inception, Development, Growth

24. Viz.ai—Pioneering Artificial Intelligence in Healthcare

Review quotes

"This slim 256-page book... authoritatively provid[es] the key current information needed to understand the role and application of artificial intelligence (AI) and machine learning in radiology.... Written by both clinical and data science experts with the aim of providing a comprehensive overview for application in clinical practice.... [the] contents are general and related to fundamental concepts, including technology and regulatory considerations, using a teaching path that also takes into account research, development and validation.... [T]he book discusses both interpretive and non-interpretive applications, and includes multiple case studies throughout.... [T]his publication may represent a good introduction to AI in radiology either for clinical radiologists and for students and trainees in the discipline, also having the ability to arouse the interest of other professionals interested in the subject." Review by Luigi Mansi, European Journal of Nuclear Medicine and Molecular Imaging, June 2026

Product details

  • Edition: 1
  • Latest edition
  • Published: November 10, 2025
  • Language: English

About the editors

AE

Adam E.M. Eltorai

Dr Adam E. M. Eltorai, MD, PhD completed his graduate studies in Biomedical Engineering and Biotechnology along with his medical degree from Brown University. His work has spanned the translational spectrum with a focus on medical technology innovation and development. Dr. Eltorai has published numerous articles and books.

Affiliations and expertise
Harvard Medical School, Boston, MA, USA

JH

James M. Hillis

Affiliations and expertise
Director of Clinical Operations Mass, General Brigham, AI Assistant Neurologist, Department of Neurology, Massachusetts General Hospital, Assistant Professor of Neurology, Harvard Medical School Boston, Massachusetts, USA

RC

Rajat Chand

Dr. Rajat Chand is a board-certified adult and pediatric interventional radiologist.

Affiliations and expertise
Interventional Radiologist, Vascular and Interventional Radiology, Independent Contractor, Austin, Texas, USA

SD

Sudhen B. Desai

Affiliations and expertise
Principal, MedTech Consultant, Interventional Radiologist, ASKD Medical, LLC, Paradise Valley, Arizona, USA

KA

Katherine P. Andriole

Affiliations and expertise
Director of Academic Research and Education, Mass General Brigham AI, Associate Professor of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

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