Overview
- Artificial intelligence (AI) and Big data are more than a digital transformation trend in healthcare
- Digital Transformation in healthcare will reshape diagnosis, disease prevention, and personalization of health services
- Privacy of medical data and the associated cybersecurity risks will be the main challenges in implementing digital healthcare strategies
Part of the book series: Integrated Science (IS, volume 9)
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About this book
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.
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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Keywords
Table of contents (12 chapters)
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Front Matter
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Back Matter
Editors and Affiliations
About the editors
Houneida Sakly is a PhD and Engineer in Medical Informatics. She is a member of the research program "deep learning analysis of Radiologic Imaging in Stanford university. Certified in Healthcare Innovation with MIT-Harvard Medical school. Her main field of research is the Data science (Artificial Intelligence, Big Data, blockchain, Internet of things...) applied in Healthcare.She is a member in the Integrated Science Association (ISA) in the Universal Scientific Education and Research Network (USERN) in Tunisia.Currently, she is serving as a lead editor for various book and special issue in the field of digital Transformation and data science in Healthcare.Recently, she has won the Best Researcher Award in the International Conference on Cardiology and Cardiovascular Medicine- San Francisco, United States.
Kristen Yeom is a Professor of Radiology at Stanford University with a research focus on clinical and translational studies of quantitative MRI. She is also on the executive board for Center for Artificial Intelligence in Medicine and Imaging at Stanford and serves as the Chair of the American Society of Pediatric Neuroradiology Grant Committee. Her recent works include radiomic and machine-learning strategies for brain tumor evaluation, as well as various computer vision tasks in clinical imaging towards precision.
Dr. Safwan Halabi is an Associate Professor of Radiology at the Northwestern University School of Medicine, Vice-Chair of Radiology Informatics, and Associate CMIO at Lurie Children’s Hospital. He also serves as the Director of Fetal Imaging at The Chicago Institute for Fetal Health. He is board-certified in Radiology with a Certificate of Added Qualification in Pediatric Radiology. He is also board-certified in Clinical Informatics. He clinically practices fetal and pediatric imaging at Lurie Children's Hospital. Dr.Halabi’s clinical and administrative leadership roles are directed at improving the quality of care,efficiency, and patient safety. He has also led strategic efforts to improve the enterprise imaging platforms at Lurie Children’s Hospital. He is a strong advocate of patient-centric care and has helped guide policies for radiology reports and image release to patients. He has published in peer-reviewed journals on various clinical and informatics topics. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support, and patient-centric health care delivery. He is currently the Chair of the RSNA Informatics Data Science Committee and serves as a Board Member for the Society for Imaging Informatics in Medicine.
Mourad Said,MD. Associate Professor in radiology and medical imaging since 2002. Member of the regional committee Africa-Middle East of the Radiological Society of North America RSNA 2014-2018. Author Reviewer for the prestigious Journal “Radiology” for many years. Different scientific presentations in RSNA meetings. He is board-certified in MRI from South Paris university. Qualifications in Pediatric/ Obstetric Radiology and MSK Imaging. He is actually interested in artificial intelligence in medical Imaging, deep learning and Radiomics with different publications.
Jayne Seekins. Clinical Assistant Professor of Radiology, Stanford University. Research interests include fellow, resident and medical student education as well as Global Health.
Moncef TAGINA. Professor of Higher education and the co-founder of the COSMOS Laboratory in the National School of Computer Sciences (ENSI) in Tunisia (ENSI).He is the Director of the Doctoral School and President of the thesis committee .
Kristen Yeom is a Professor of Radiology at Stanford University with a research focus on clinical and translational studies of quantitative MRI. She is also on the executive board for Center for Artificial Intelligence in Medicine and Imaging at Stanford and serves as the Chair of the American Society of Pediatric Neuroradiology Grant Committee. Her recent works include radiomic and machine-learning strategies for brain tumor evaluation, as well as various computer vision tasks in clinical imaging towards precision.
Dr. Safwan Halabi is an Associate Professor of Radiology at the Northwestern University School of Medicine, Vice-Chair of Radiology Informatics, and Associate CMIO at Lurie Children’s Hospital. He also serves as the Director of Fetal Imaging at The Chicago Institute for Fetal Health. He is board-certified in Radiology with a Certificate of Added Qualification in Pediatric Radiology. He is also board-certified in Clinical Informatics. He clinically practices fetal and pediatric imaging at Lurie Children's Hospital. Dr.Halabi’s clinical and administrative leadership roles are directed at improving the quality of care,efficiency, and patient safety. He has also led strategic efforts to improve the enterprise imaging platforms at Lurie Children’s Hospital. He is a strong advocate of patient-centric care and has helped guide policies for radiology reports and image release to patients. He has published in peer-reviewed journals on various clinical and informatics topics. His current academic and research interests include imaging informatics, deep/machine learning in imaging, artificial intelligence in medicine, clinical decision support, and patient-centric health care delivery. He is currently the Chair of the RSNA Informatics Data Science Committee and serves as a Board Member for the Society for Imaging Informatics in Medicine.
Mourad Said,MD. Associate Professor in radiology and medical imaging since 2002. Member of the regional committee Africa-Middle East of the Radiological Society of North America RSNA 2014-2018. Author Reviewer for the prestigious Journal “Radiology” for many years. Different scientific presentations in RSNA meetings. He is board-certified in MRI from South Paris university. Qualifications in Pediatric/ Obstetric Radiology and MSK Imaging. He is actually interested in artificial intelligence in medical Imaging, deep learning and Radiomics with different publications.
Jayne Seekins. Clinical Assistant Professor of Radiology, Stanford University. Research interests include fellow, resident and medical student education as well as Global Health.
Moncef TAGINA. Professor of Higher education and the co-founder of the COSMOS Laboratory in the National School of Computer Sciences (ENSI) in Tunisia (ENSI).He is the Director of the Doctoral School and President of the thesis committee .
Bibliographic Information
Book Title: Trends of Artificial Intelligence and Big Data for E-Health
Editors: Houneida Sakly, Kristen Yeom, Safwan Halabi, Mourad Said, Jayne Seekins, Moncef Tagina
Series Title: Integrated Science
DOI: https://doi.org/10.1007/978-3-031-11199-0
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-11198-3Published: 03 January 2023
Softcover ISBN: 978-3-031-11201-0Published: 04 January 2024
eBook ISBN: 978-3-031-11199-0Published: 01 January 2023
Series ISSN: 2662-9461
Series E-ISSN: 2662-947X
Edition Number: 1
Number of Pages: X, 251
Number of Illustrations: 3 b/w illustrations, 64 illustrations in colour
Topics: Biomedicine, general, Health Care Management, Statistics, general, Data Structures and Information Theory, Artificial Intelligence, Bioinformatics