Trends of Artificial Intelligence and Big Data for E-Health

This book aims to present evidence of the crucial importance of artificial intelligence and big data for medical decision making and data analysis in different fields of E-Health such as radiology, cancer prevention, drugs discovery, COVID-19 detection, AI and blockchain, cardiac imaging, cybersecurity, etc. Big data analytics and artificial intelligence have the potential to lead the methodology of healthcare providers using sophisticated technologies for accurate analysis of clinical data repositories and assist in making informed decisions, while ensuring confidentiality and data security. The challenges of intelligent Health depend basically on the opportunities provided by the community of experts to make health systems more sustainable. In intelligent healthcare, Big Data is based on massive data collected routinely or automatically, and stored electronically. The re-usability of this data could include links between existing databases to improve theperformance and efficiency of the health system.Big data and artificial intelligence data will produce significant and accurate results to support medical decision making. The process would benefit from patient's data and their clinical history to support the experts in providing a more personalized medical overview. The intelligent health approach has the potential to allow a close surveillance of the patient's progress during therapy.

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

Auteur

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 .

Contenu

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.