Η ομιλία θα πραγρατοποιηθεί αποκλειστικά διαδικτυακά
Title: Τεχνητή Νοημοσύνη στην Ιατρική Ακριβείας
Speaker: Prof. Polydoros Kampaktsis (Aristotle University of Thessaloniki)
Short Bio: Polydoros Kampaktsis completed undergraduate studies in Medicine and Electrical & Computer Engineering
at the Aristotle University of Thessaloniki in Greece, where he also received a Master’s degree in Medical Informatics. He subsequently completed a clinical PhD in Structural Heart Disease at the University of Athens Medical School. Polydoros completed his
post-graduate medical training in Internal Medicine, Cardiology, Interventional Cardiology and Echocardiography in top academic centers in the US. He was an Assistant Professor of Medicine at the Columbia University Irving Medical Center, Division of Cardiology
for 3 years. He then returned to training to complete a clinical fellowship in Structural Heart Disease at the Hackensack University Medical Center, NJ. He currently also serves as a Visiting Professor at the Aristotle University of Thessaloniki Medical School.
Polydoros is a practicing Cardiologist who performs two types of research. On one hand, he is dedicated to clinical research with a main focus on Structural Heart Disease and valvulopathies, Coronary Artery Disease and Cardiovascular Imaging. On the other hand,
he has been investigating potential applications of Machine Learning in Cardiovascular Medicine. More specifically, he has been collaborating with Data Scientists in Europe and Greece to evaluate the use of Machine Learning in clinical outcomes research, clinical
phenotyping and particularly the development of novel applications to address daily clinical needs in Cardiology. He is the co-inventor of a new Machine Learning-based technique for quantitative analysis of the right ventricle using two-dimensional echocardiography
only, for which he holds an international patent.
Polydoros believes that careful selection of ideas and data, excellent multidisciplinary collaboration and hard work can lead to ground breaking Machine Learning applications in Cardiology and Medicine that can move the field forward and have an impact in the
daily care of patients. He is inspired by excellence in practicing Cardiology and performing meaningful research.
Meeting Link: https://uoa.webex.com/uoa/j.php?MTID=m54e2d2df887a011b3da72379a3ac2de8
Meeting Number: 2783 870 8869
Password: r72dGJPfgi2
Join by video system
Dial This email address is being protected from spambots. You need JavaScript enabled to view it.
You can also dial 62.109.219.4 and enter your meeting number.
