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008			230311s2022 sz | s |||| 0|eng d
020			_a9783031111990
024	7		_a10.1007/978-3-031-11199-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aR859.7.A78 _b2022 EB
245	0	0	_aTrends of Artificial Intelligence and Big Data for E-Health _cedited by Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 251 páginas) _b67 ilustraciones, 64 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aIntegrated Science _x2662-947X _v9
505	0		_a1. AI and Big Data for Intelligent Health: Promise and Potential -- 2. AI and Big Data for Cancer Segmentation, Detection and Prevention -- 3. Radiology, AI and Big Data: Challenges and Opportunities for Medical Imaging -- 4. Neuroradiology: Current Status and Future Prospects -- 5. Big Data and AI in Cardiac Imaging -- 6. Artificial Intelligence and Big data for COVID-19 Diagnosis -- 7. AI and Big Data for Drug Discovery -- 8. Blockchain of IoMT (BIoMT): A New Paradigm for COVID-19 Pandemic: Application, Architecture, Technology, and Security -- 9. AI and Big Data for Therapeutic Strategies in Psychiatry -- 10. Distributed Learning in Healthcare -- 11. Cybersecurity in Healthcare -- 12. Radiology and Radiomics: Towards oncology Prediction with IA and Big Data.
520			_aThis book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and clinical decision support. The capacity to recognize patterns and transform large amounts of data into usable information for precision medicine assists healthcare professionals in achieving these objectives. Intelligent Health has the potential to monitor patients at risk with underlying conditions and track their progress during therapy. Some of the greatest challenges in using these technologies are based on legal and ethical concerns of using medical data and adequately representing and servicing disparate patient populations. One major potential benefit of this technology is to make health systems more sustainable and standardized. Privacy and data security, establishing protocols, appropriate governance, and improving technologies will be among the crucial priorities for Digital Transformation in Healthcare.
988			_aSpringer_BiomedLife_2022
650		7	_2embne _9421371 _aInteligencia artificial en medicina
650		7	_2embne _9421154 _aInformática médica
776	0	8	_iPrinted edition: _z9783031111983
776	0	8	_iPrinted edition: _z9783031112003
776	0	8	_iPrinted edition: _z9783031112010
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-11199-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI