The Radiology AI Handbook - Edition 1 - Edited by Adam E.M. Eltorai, James M. Hillis, Rajat Chand, Sudhen B. Desai and Katherine P. Andriole Elsevier Educate

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The Radiology AI Handbook,

Edition 1

Edited by Adam E.M. Eltorai, James M. Hillis, Rajat Chand, Sudhen B. Desai and Katherine P. Andriole

Publication Date: 22 Dec 2025

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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.

About the author

Edited by Adam E.M. Eltorai, Harvard Medical School, Boston, MA, USA; James M. Hillis; Rajat Chand; Sudhen B. Desai and Katherine P. Andriole

PART I: BACKGROUND
1. Market overview, growth, and why
2. Fundamental concepts (e.g. AI, ML), vocabulary
3. Technology principles (e.g. modelling, learning methods, deep learning, sparse coding, big data)

PART II: APPLICATIONS
1. Breast (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
2. Cardiovascular (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
3. Chest (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
4. Emergency (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
5. Gastrointestinal (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
6. Genitourinary (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
7. Head and neck (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
8. Musculoskeletal (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
9. Neuroradiology (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
10. Paediatric (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
11. Interventional (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)
12. Nuclear (Current FDA-approved applications; existing companies; applications under development; opportunities, defined needs)

PART III: DEVELOP YOUR APPLICATION
13. Problem (ideation process, what problem are you solving, for whom, value prop, special sauce)
14. Team (who you need, roles)
15. R&D, validation process
16. Regulatory, quality, ethical, legal

PART IV: COMMERCIALIZATION
17. Routes of commercialization
18. Funding- who, how, economics, power
19. Cases studies (stories of successful rad AI ventures)

ISBN: 9780323877602

Page Count: 256

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Radiologists, residents, trainees, clinicians, emergency medicine physicians

Book Details

Key Features

About the author