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Full Description
This edited book focuses on the application of AI and data analytics within the three specialisms of medical imaging: diagnostic radiography (including fluoroscopy, computed tomography, breast imaging, ultrasound, and magnetic resonance imaging), radiotherapy and oncology, and nuclear medicine and molecular imaging.
Artificial Intelligence and Data Analytics in Medical Imaging leverages the expertise of key practitioners, academics, and researchers who are recognized leaders in their respective fields. The chapters cover essential topics including imaging modalities, treatment planning, ethics, and future recommendations. The editors incorporate insights from recent publications and clinical practice, addressing how emerging technologies should be managed, implemented, and adapted in healthcare settings. Each chapter maintains a patient-centered focus while connecting to key literature in the field.
This book acts as a cornerstone for undergraduate students, but importantly 'signpost' to other key texts within the field of medical imaging. Further, academics will also find this text useful as it aims to enrich scholarly learning, teaching and assessment to healthcare programs nationally and internationally.
Contents
Chapter 1 Artificial Intelligence in Medical Imaging Chapter 2 Preprocessing of Medical imaging Data Chapter 3 Artificial Intelligence and Data Analytics in Medical Imaging: Tools and Packages in Clinical Practice Chapter 4 Balancing trust and reliance: Understanding the Human-AI interaction to ensure responsible use of innovation and advanced technologies in radiography Chapter 5 Brain MRI Segmentation Chapter 6 Centring the Patient in Implementing AI for Medical Imaging Chapter 7 Artificial Intelligence and Data Analytics in Medical Imaging for the Diagnosis of Endometriosis Chapter 8 The Role of Artificial Intelligence in Forensic Radiology Chapter 9 Artificial Intelligence for Medical Imaging in Radiation Therapy Chapter 10 Artificial Intelligence for MRI-based Diagnosis of Prostate Cancer in Clinical Practice Chapter 11 Artificial Intelligence in Mammography Chapter 12 Integrating Artificial Intelligence into Medical Imaging Curriculum: Challenges, Applications and Future Directions Chapter 13 Business Analytics for Radiology: A Narrative Review Chapter 14 Radiographers and computer programmers: Finding collaborative ways to enhance clinical outcomes Chapter 15 Brain Age Prediction: Methods, Models and Applications
