Artificial Intelligence for HealthcareAIH · Ethical AI in medicine

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
10
Chapters
36
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AIH Academic Book Initiative Effective Implementation of AI in Radiology 10 Parts · 36 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 36 chapters, arranged in ten parts, follow artificial intelligence in radiology from first principles to responsible use in patient care:

  • Part IRadiology, Its Origins, and Clinical Foundations
  • Part IIArtificial Intelligence for Medical and Healthcare Professionals
  • Part IIIResearch Methodology, Data Science, and Critical Evaluation
  • Part IVAI Technology, Imaging Interpretation, and Radiology Workflows
  • Part VAI in Primary Care, Point-of-Care Imaging, Emergency Medicine, and Teleradiology
  • Part VIOrgan-System and Disease-Specific Imaging I: Neurological, Head and Neck, Thoracic, Cardiovascular, and Musculoskeletal Applications
  • Part VIIOrgan-System and Disease-Specific Imaging II: Breast, Abdominal, Genitourinary, Gynecologic, Obstetric, and Pediatric Applications
  • Part VIIIOncologic, Molecular, Radiation, and Image-Guided Therapeutic Applications
  • Part IXEconomics, Entrepreneurship, and Business Innovation in Radiology AI
  • Part XClinical Translation, Safety, Ethics, Governance, and the Future of Radiology AI

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 10–20, 2026

The book is planned for release at RSNA 2026.

Table of contents

Part IRadiology, Its Origins, and Clinical Foundations2 chapters
  1. 01Origins and Evolution of Radiology: From X-Rays to Artificial Intelligence
  2. 02Imaging Physics, Acquisition, and Reconstruction: Foundations for Artificial Intelligence in Radiology
Part IIArtificial Intelligence for Medical and Healthcare Professionals3 chapters
  1. 03Foundations of Artificial Intelligence and Machine Learning in Radiology
  2. 04AI Model Development, Training Strategies, Optimization, and Validation
  3. 05AI Education and Competency-Based Curriculum for Medical and Healthcare Professionals: AI Literacy, Clinical Skills, Simulation, Assessment, and Lifelong Learning
Part IIIResearch Methodology, Data Science, and Critical Evaluation2 chapters
  1. 06Radiology AI Data: Curation, Annotation, Reference Standards, and Dataset Governance
  2. 07Research Design, Clinical Trials, and Evidence Synthesis for Radiology AI: Reader Studies, Diagnostic Accuracy, Systematic Reviews, Meta-Analysis, Reporting Standards, and Risk of Bias
Part IVAI Technology, Imaging Interpretation, and Radiology Workflows3 chapters
  1. 08AI-Enhanced Image Acquisition, Reconstruction, and Quality Assurance
  2. 09AI-Assisted Image Interpretation: Detection, Classification, Segmentation, and Quantitative Analysis
  3. 10Artificial Intelligence in Radiology Workflow, Reporting, and Clinical Decision Support: Intelligent Worklists, Natural Language Processing, Large Language Models, Communication, and Quality Assurance
Part VAI in Primary Care, Point-of-Care Imaging, Emergency Medicine, and Teleradiology4 chapters
  1. 11Artificial Intelligence for Primary Care and Nonradiologist Clinicians: Imaging Appropriateness, Point-of-Care Decision Support, Referral Pathways, Incidental Findings, and Collaborative Care
  2. 12Artificial Intelligence in Ultrasound and Point-of-Care Imaging: Acquisition Guidance, Image Interpretation, Quantification, Clinical Applications, and Implementation
  3. 13Artificial Intelligence in Emergency and Trauma Radiology: Time-Critical Detection, Triage, Decision Support, and Clinical Integration
  4. 14Artificial Intelligence in Teleradiology and Distributed Imaging Services: Remote Interpretation, Intelligent Work Allocation, Quality Assurance, Workforce Integration, and Access to Care
Part VIOrgan-System and Disease-Specific Imaging I: Neurological, Head and Neck, Thoracic, Cardiovascular, and Musculoskeletal Applications5 chapters
  1. 15Artificial Intelligence in Neuroradiology: Stroke, Brain Tumors, Neurodegeneration, and Spine Imaging
  2. 16Head, Neck, and Maxillofacial Imaging: Oncology, ENT, Dental, and Craniofacial Applications
  3. 17Artificial Intelligence in Thoracic Imaging: Chest Radiography, Lung Cancer Screening, Pulmonary Nodules, and Diffuse Lung Disease
  4. 18Artificial Intelligence in Cardiovascular Imaging: Coronary CT, Cardiac MRI, Functional Assessment, and Risk Prediction
  5. 19Artificial Intelligence in Musculoskeletal Imaging: Fracture Detection, Joint Disease, Sports Injuries, Spine, Bone Tumors, and Quantitative Imaging
Part VIIOrgan-System and Disease-Specific Imaging II: Breast, Abdominal, Genitourinary, Gynecologic, Obstetric, and Pediatric Applications5 chapters
  1. 20Artificial Intelligence in Breast Imaging: Screening Mammography, Tomosynthesis, Ultrasound, MRI, Risk Prediction, and Clinical Integration
  2. 21Artificial Intelligence in Abdominal and Gastrointestinal Imaging: Liver, Pancreas, Bowel, and Quantitative Abdominal Imaging
  3. 22Artificial Intelligence in Genitourinary and Pelvic Imaging: Prostate, Kidney, Urinary Tract, Bladder, and Pelvic Applications
  4. 23Gynecologic and Obstetric Imaging: Uterus, Adnexa, Placenta, Fetal Imaging, and Maternal Safety
  5. 24Artificial Intelligence in Pediatric Imaging: Developmental Anatomy, Diagnostic Applications, Dose Optimization, and Patient Safety
Part VIIIOncologic, Molecular, Radiation, and Image-Guided Therapeutic Applications5 chapters
  1. 25Oncologic Imaging and Radiomics: Detection, Staging, Prognosis, Treatment Response, Radiogenomics, and Opportunistic Biomarkers
  2. 26Nuclear Medicine and Molecular Imaging: PET, SPECT, Hybrid Imaging, Reconstruction, Quantification, and Theranostics
  3. 27Artificial Intelligence in Radiation Oncology: Automated Contouring, Treatment Planning, Adaptive Radiotherapy, Quality Assurance, and Outcome Prediction
  4. 28Artificial Intelligence in Interventional Radiology: Image-Guided Procedures, Navigation, Robotics, Procedural Planning, Safety, and Clinical Outcomes
  5. 29Robotics and Autonomous Image-Guided Intervention: Percutaneous Systems, Endovascular Navigation, Robotic Control, Simulation, Human Oversight, and Procedural Safety
Part IXEconomics, Entrepreneurship, and Business Innovation in Radiology AI2 chapters
  1. 30Economics and Value-Based Evaluation of Radiology AI: Cost-Effectiveness, Budget Impact, Productivity, Return on Investment, and Health-System Outcomes
  2. 31Radiology AI Entrepreneurship and Commercialization: Product Development, Intellectual Property, Business Models, Clinical Validation, Strategic Partnerships, and Responsible Scaling
Part XClinical Translation, Safety, Ethics, Governance, and the Future of Radiology AI5 chapters
  1. 32Has AI Improved Radiology Care? From Benchmark Accuracy to Clinical Effectiveness, Workflow, and Patient Outcomes
  2. 33Safety Engineering for Radiology AI: Robustness, Distribution Shift, Automation Bias, Monitoring, Cybersecurity, and Incident Response
  3. 34Ethics, Fairness, Privacy, and Equity in Imaging AI
  4. 35Regulation, Liability, Governance, Procurement, Reimbursement, and Accountability
  5. 36The 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 10–20, 2026.

Apply to Become a Chapter Reviewer

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