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008			190129s2019 gw a s |||| 0|eng d
020			_a9783319948782
024	7		_a10.1007/978-3-319-94878-2 _2doi
040			_bspa _cES-MaUEC _dES-MaUEC
050		4	_aRC78.7.D53 _b2019 EB
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 _c2019
300			_a1 recurso en línea (XV, 373 páginas) _b104 ilustraciones, 81 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
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			_aSpringer_Medicine_2019
650		7	_2embne _9139972 _aDiagnóstico por imagen _xProceso de datos
650		7	_2embne _9421371 _aInteligencia artificial en medicina
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
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 Madrid)
942			_2lcc _cLE
998			_dz _b02/2023 _ea