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AIH Academic Book Initiative

Effective Implementation of AI in Radiology

Exploring how artificial intelligence can be developed, evaluated, translated into clinical practice, and implemented safely and responsibly across radiology.

Parts
6
Chapters
30
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AIH Academic Book Initiative Effective Implementation of AI in Radiology 6 Parts · 30 Chapters Artificial Intelligence for Healthcare

About the book

Effective Implementation of AI in Radiology brings clinical, technical, methodological, ethical and legal perspectives together in one resource. Its 30 chapters, arranged in six parts, follow artificial intelligence in radiology from first principles to responsible use in patient care:

  • Part IRadiology's origins and clinical foundations
  • Part IIArtificial intelligence, machine learning and deep learning for medical professionals
  • Part IIIAI technology, imaging modalities and radiology workflows
  • Part IVResearch methodology and critical evaluation
  • Part VOrgan-system and disease-specific applications
  • Part VIClinical translation, safety, ethics and accountability

Intended reviewer expertise

AIH invites appropriately qualified experts to review chapters that match their knowledge and experience, including:

  • Radiology and diagnostic imaging
  • Artificial intelligence and machine learning
  • Deep learning and medical image analysis
  • Computer science and computer engineering
  • Data science and medical informatics
  • Clinical medicine
  • Research methodology and evidence synthesis
  • Medical ethics and responsible AI
  • Healthcare law, regulation and liability
  • Healthcare implementation and related disciplines

Reviewer appointments and chapter assignments are confirmed by the AIH editorial team. Applying does not by itself guarantee an appointment or publication credit.

Editorial and media support

AIH will coordinate the review process and provide media-team support for suitable illustrations, diagrams, visual explanations, and other chapter visuals.

Review timeline

Review periodNovember 1–10, 2026

The book is planned for release at RSNA 2026.

Table of contents

Part IRadiology, Its Origins, and Clinical Foundations2 chapters
  1. 01From Röntgen to Digital and Hybrid Imaging: The Evolution of Radiology
  2. 02How Images Become Clinical Evidence: Imaging Physics, Modalities, Contrast, Informatics, and Workflow
Part IIArtificial Intelligence for Medical Professionals3 chapters
  1. 03Artificial Intelligence and Machine Learning for Clinicians: Concepts, Learning Paradigms, and Model Development
  2. 04Deep Learning for Medical Images: Neural Networks, Convolutional Networks, Attention, Transformers, and Segmentation
  3. 05Data-Centric, Multimodal, and Generative AI: Self-Supervision, Transfer, Federated Learning, Foundation Models, Large Language Models, and Generalization
Part IIIAI Technology, Imaging Modalities, and Radiology Workflows3 chapters
  1. 06AI at the Scanner: Acquisition, Protocoling, Reconstruction, Denoising, Super-Resolution, Dose, and Quality Assurance
  2. 07AI for Image Interpretation: Detection, Classification, Segmentation, Quantification, Radiomics, and Imaging Biomarkers
  3. 08AI in the Reading Room: Triage, Prioritization, Reporting, Natural Language Processing, Large Language Models, Operations, and Human–AI Collaboration
Part IVResearch Methodology and Critical Evaluation3 chapters
  1. 09Evaluating Diagnostic and Predictive AI: Study Design, Reference Standards, Accuracy, Calibration, and External Validation
  2. 10From Reader Studies to Clinical Trials: Human–AI Comparisons, Pragmatic Evaluation, Implementation Science, Health Economics, and Surveillance
  3. 11Evidence Synthesis and Research Standards: Systematic Review, Meta-analysis, Reporting Guidelines, and Risk-of-Bias Appraisal
Part VOrgan-System and Disease-Specific Applications14 chapters
  1. 12Neuroimaging and the Central Nervous System: Brain, Stroke, Hemorrhage, Tumor, Neurodegeneration, and Spine
  2. 13Head, Neck, and Maxillofacial Imaging: Oncology, ENT, Dental, and Craniofacial Applications
  3. 14Thoracic and Pulmonary Imaging: Chest Radiography, Lung CT, Infection, Interstitial Disease, and Lung Cancer Screening
  4. 15Cardiac and Vascular Imaging: Coronary Disease, Cardiac Function, Aorta, Peripheral Vessels, and Cerebrovascular Imaging
  5. 16Breast Imaging: Screening, Diagnosis, Risk, Density, Tomosynthesis, Ultrasound, and MRI
  6. 17Gastrointestinal, Hepatobiliary, Pancreatic, and Splenic Imaging
  7. 18Genitourinary Imaging: Kidney, Urinary Tract, Prostate, Testis, and Male Pelvis
  8. 19Gynecologic and Obstetric Imaging: Uterus, Adnexa, Placenta, Fetal Imaging, and Maternal Safety
  9. 20Musculoskeletal Imaging: Trauma, Degeneration, Inflammation, Sports Injury, Bone Tumors, and Quantitative MRI
  10. 21Pediatric and Congenital Imaging: Age-Specific Models, Radiation, Rare Disease, and Developmental Anatomy
  11. 22Emergency Radiology and Trauma: Time-Critical Detection, Triage, Whole-Body Imaging, and Acute Workflow
  12. 23Oncologic Imaging and Radiomics: Detection, Staging, Prognosis, Treatment Response, Radiogenomics, and Opportunistic Biomarkers
  13. 24Nuclear Medicine and Molecular Imaging: PET, SPECT, Hybrid Imaging, Reconstruction, Quantification, and Theranostics
  14. 25Interventional Radiology: Planning, Navigation, Image Guidance, Robotics, Dose Optimization, and Outcome Prediction
Part VIClinical Translation, Safety, Ethics, and Accountability5 chapters
  1. 26Has AI Improved Radiology Care? From Benchmark Accuracy to Clinical Effectiveness, Workflow, and Patient Outcomes
  2. 27Safety Engineering for Radiology AI: Robustness, Distribution Shift, Automation Bias, Monitoring, Cybersecurity, and Incident Response
  3. 28Ethics, Fairness, Privacy, and Equity in Imaging AI
  4. 29Regulation, Liability, Governance, Procurement, Reimbursement, and Accountability
  5. 30The Next Radiology AI: Multimodal Foundation Models, Agentic Systems, Autonomy, Sustainability, Workforce, and Responsible Adoption

Interested in reviewing a chapter?

Choose the chapters that match your expertise. Review period: November 1–10, 2026.

Apply to Become a Chapter Reviewer

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