M-Tahar KECHADI} received his PhD and MSc in Computer Science from the University of Lille 1, France. He is currently a full professor of data science at the School of Computer Science, University College Dublin. He is a principal investigator at the Insight Centre for Data Analytics.The core and central focus of his research for the last decade has been managing (including privacy) and analyzing data quickly and efficiently.

Currently, my research interests are primarily in Big Data Analytics and its applications to real-world applications (Digital Healthcare and Digital Agriculture), Distributed Mining techniques and models and their execution environments and applications, Cloud computing and services for supporting data access, management, and mining processes, and Digital Forensics and Cybercrime Investigations and Cybersecurity. I have been a visiting professor at many Universities (Liverpool, Fuzhou, Artois, …). He serves on the scientific committees for several international conferences and has organized and hosted one of the leading conferences in his area. He is a member of IEEE and ACM.

Abstract: The Impact of Data and AI on Healthcare.

In the healthcare sector, huge quantities of data about patients and their medical conditions have been gathered through clinical databases and various other healthcare processes. Currently, it records nearly all aspects of care, including patient personal information, clinical trials, hospital records, diagnosis, medication, test results, imaging data, costs, administrative reports, etc. Like in other application domains, the big data revolution also holds great promise in the area of healthcare, as the available data about individual patients is very rich and hides crucial knowledge that can be exploited to improve patients’ care while reducing its cost. For instance, by 2025, the annual growth rate of healthcare data will be as much as 36% of the world’s data, and it is expected to grow 50 times more in the next five years. Turning this massive amount of data into knowledge that can be used to identify needs, predict and prevent critical patients’ conditions, and help practitioners make rapid and accurate decisions is not only a desire but an urgent and crucial necessity. Therefore, healthcare organizations must have the ability to manage and analyze their data in a rapid and efficient manner to answer several critical questions related to diseases, treatments, patients’ behaviors, and care management. However, building such a system faces huge challenges: 1) data complexity, 2) Privacy, security, ethical, legal, and social issues, and 3) Interoperability, portability, and compatibility. We will discuss all these challenges and the requirements of the AI-based healthcare ecosystem. This will lead us to describe some innovative methodologies for building such a smart ecosystem to face the healthcare challenges of the next decade or so.