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020 _a9783319948782
024 7 _a10.1007/978-3-319-94878-2
_2doi
040 _bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aRC78.7
_b.A78 2019EB
245 0 0 _aArtificial Intelligence in Medical Imaging.
_bOpportunities, Applications and Risks
_cedited by Erik R. Ranschaert, Sergey Morozov, Paul R. Algra.
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2019.
300 _aXV, 373 páginas 104 ilustraciones, 81 ilustraciones a color
336 _2rdacontent
_aTexto (visual)
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aMedicine (Springer-11650)
505 0 _aPART I: INTRODUCTION: Introduction: Game changers in radiology -- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare -- History and evolution of A.I. in medical imaging -- Deep Learning and Neural Networks in imaging: basic principles -- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers -- How to develop A.I. applications -- Validation of A.I. applications -- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging -- Data mining in radiology -- Image biobanks -- The quest for medical images and data -- Clearance of medical images and data -- Legal and ethical issues in AI -- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases -- Cardiac diseases -- Breast cancer -- Neurological diseases -- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis -- Value of structured reporting for A.I. -- The role of A.I. for clinical trials -- Market and economy of A.I.: evolution -- The role of an A.I. ecosystem for radiology -- Advantages and risks of A.I. for radiologists -- Re-thinking radiology.
520 3 _aThis book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
988 _aPrimersemestre_2019_Medicine
653 0 _aRadiology, Medical.
653 0 _aInformation systems.
653 0 _aMedical records
700 1 _aRanschaert, Erik R.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMorozov, Sergey.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAlgra, Paul R.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
_9106937
776 0 8 _iPrinted edition:
_z9783319948775
776 0 8 _iPrinted edition:
_z9783319948799
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-94878-2
_zAcceso a este recurso digital (usuarios Universidad Europea de Valencia)
942 _2lcc
_cLE
998 _db
_feng
_ggw
_h0
_el