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Trends of artificial intelligence and big data for e-health

Record Type:	Electronic resources : Monograph/item
Title/Author:	Trends of artificial intelligence and big data for e-healthedited by Houneida Sakly ... [et al.].
other author:	Sakly, Houneida.
Published:	Cham :Springer International Publishing :2022.
Description:	x, 251 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligenceMedical applications.
Online resource:	https://doi.org/10.1007/978-3-031-11199-0
ISBN:	9783031111990$q(electronic bk.)

Trends of artificial intelligence and big data for e-health
Trends of artificial intelligence and big data for e-health[electronic resource] /edited by Houneida Sakly ... [et al.]. - Cham :Springer International Publishing :2022. - x, 251 p. :ill., digital ;24 cm. - Integrated science,v. 92662-947X ;. - Integrated science ;v.2..

1. 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.

This 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.

ISBN: 9783031111990$q(electronic bk.)

Standard No.: 10.1007/978-3-031-11199-0doiSubjects--Topical Terms:

237917
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / T74 2022

Dewey Class. No.: 610.285

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505 0 $a 1. 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 $a This 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.
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