PART I: INTRODUCTION 1 Introduction: Game changers in radiology PART II: TECHNIQUES 2 The role of medical imaging computing, informatics and machine learning in healthcare 2 History and evolution of A.I. in medical imaging 3 Deep Learning and Neural Networks in imaging: basic principles PART III DEVELOPMENT of AI APPLICATIONS 4 Imaging biomarkers 5 How to develop A.I. applications 6 Validation of A.I. applications PART IV: BIG DATA IN RADIOLOGY 7 The value of enterprise imaging 8 Data mining in radiology 9 Image biobanks 10 The quest for medical images and data 11 Clearance of medical images and data 12 Legal and ethical issues in AI PART V: CLINICAL USE OF A.I. IN RADIOLOGY 13 Pulmonary diseases 14 Cardiac diseases 15 Breast cancer 16 Neurological diseases PART VI: IMPACT of A.I. on RADIOLOGY 17 Applications of A.I. beyond image analysis 18 Value of structured reporting for A.I. 19 The role of A.I. for clinical trials 20 Market and economy of A.I.: evolution 21 The role of an A.I. ecosystem for radiology 22 Advantages and risks of A.I. for radiologists 23 Re-thinking radiology Skryť popis