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AI and Digital Innovations in Healthcare: Transforming Medical Practice and Research


AUTh
Enrollment is Closed

Course description

This course is designed to provide a comprehensive overview of the intersection between technology and healthcare, offering students an immersive learning experience into the current and future landscape of medical practice and research. This course aims to equip students with the knowledge and skills needed to navigate and contribute to the rapidly evolving field of healthcare technology. The curriculum is structured into six distinct modules, each focusing on a different aspect of technology's impact on or correlation with healthcare.

The first module delves into Digital Health, Telehealth, and Telemedicine, exploring how digital technologies are transforming patient care, enabling remote consultations, and improving accessibility to healthcare services. This foundation sets the stage for the subsequent modules, where students will gain a deeper understanding of technology's role in healthcare.

Introduction to Artificial Intelligence, the second module, provides a primer on AI concepts, tools, and applications in healthcare. Students will learn the history of AI, its core areas of research and some of its more impressive applications.

The third module, Advancements in Healthcare Data, Emerging Sources, and Sensor Technologies, examines the explosion of healthcare data from electronic health records, wearables, and other sensor technologies. It discusses the implications of this data deluge for patient care, research, and privacy concerns.

Machine Learning and Healthcare Applications, the fourth module, dives deeper into machine learning techniques, focusing on their applications in predictive analytics, disease prediction, and health monitoring, highlighting real-world use cases and the ethical considerations involved.

The fifth module about Trustworthy and Explainable AI module addresses the critical need for AI systems to be transparent, understandable, and ethically responsible, ensuring that healthcare professionals can trust and effectively integrate AI into clinical practice.

Finally, the course concludes with Engagement of Patients & HCPs in Decentralized Clinical Trials, exploring how digital tools and platforms are revolutionizing the conduct of clinical trials, making them more patient-centric and efficient.

Course Staff

Panagiotis Bamidis

Professor of Medical Physics, Informatics and Medical Education & Director of the Lab of Medical Physics and Digital Innovation in the School of Medicine, Aristotle University of Thessaloniki

Antonis Billis

Antonis Billis is a Post-Doc Research Fellow at the Lab of Medical Physics & Digital Innovation in the School of Medicine, Aristotle University of Thessaloniki. He focuses his research in remote patient monitoring, development and evaluation of assistive technologies for older adults and vulnerable populations, Living Lab methodologies in the health and wellbeing domain, and cancer informatics.

Emmanouil Rigas

Emmanouil Rigas is a Postdoctoral Research Associate at the Lab of Medical Physics and Digital Innovation in the School of Medicine, Aristotle University of Thessaloniki. He is a computer scientist and his research is focused in the areas of Artificial Intelligence and Knowledge Representation.

George Petridis

George Petridis is a research associate at the Lab of Medical Physics and Digital Innovation in the school of Medicine, Aristotle University of Thessaloniki. He has a background in computer engineering and data science.

Paraskevas Lagakis

Paraskevas Lagakis is a research associate at the Lab of Medical Physics and Digital Innovation in the School of Medicine, Aristotle University of Thessaloniki. He has been working as a lead software architect in various digital health projects in the past.

Ilias K. Kokkinidis

Ilias K. Kokkinidis is a Research Associate at the Lab of Medical Physics and Digital Innovation in the School of Medicine, Aristotle University of Thessaloniki. He specializes in artificial intelligence, developing machine and deep learning prediction models and general data analysis.