Kyriaki Theodorou, Papadimitroulas Panagiotis, Koutsiaris Aristotelis, Karpetas Georgios, Kylindris Thomas
ECTS:
3.00
COURSE TYPE
YP | Background-Skills Development
TEACHING SEMESTER
1st SEMESTER
WEEKLY TEACHING HOURS:
4 HOURS
Total Time (Teaching Hours + Student Workload)
81 HOURS
PREREQUIRED COURSES:
NO
LANGUAGE OF TEACHING AND EXAMS
GREEK
AVAILABLE TO ERASMUS STUDENTS
YES
SEMESTER LECTURES:
DETAILS/LECTURES
TEACHING AND LEARNING METHODS:
Face-to-face, Distance Learning, etc.
Lectures & Theoretical Presentation
Laboratory Exercises with Real Data
Case Studies, Group Presentations
Application of VR/AR Technologies and Interactive Platforms
Use of individual workstations (PC) per student
STUDENT EVALUATION
Short-answer questions
Public presentation of assignments
Problem solving
Participation and attendance with feedback
Written assignments / presentations
Laboratory Exercises
Final examination (written)
Objective Objectives/Desired Results:
The course “Medical Informatics” aims to introduce students to the basic principles of modern Medical Informatics, with emphasis on the processing of medical data, artificial intelligence, telemedicine applications, and virtual/augmented reality in medicine. Students will gain knowledge of the technologies used in clinical practice, health data management and clinical decision support. The course combines theoretical lectures with practical applications, allowing students to understand and use modern technologies in medical practice.
Upon successful completion of the course, students will be able to:
Understand the basic concepts of Medical Informatics and its role in clinical practice and health administration.
Identify the types and sources of medical data and apply basic processing and analysis techniques.
Evaluate the use of Artificial Intelligence (AI) algorithms, Clinical Decision Support Systems (CDSS), and Telemedicine.
Apply basic methods of image processing and signal analysis.
Explain the principles and uses of Virtual and Augmented Reality in medical education and practice.
Discuss ethical, legal and social issues arising from the application of AI and digital technology in health.
Alignment with EQF – Level 6:
Knowledge: Advanced knowledge in the organization and processing of medical data, AI systems, EHR, CDSS.
Skills: Use of data processing tools, interpretation of clinical examples, basic handling of VR/AR.
Competences: Decision-making based on data, collaboration in teamwork, development of a holistic understanding of the digital ecosystem in medicine.
General Abilities
General Competence
How It Is Served by the Course
Search, analysis and synthesis of data and information, using the necessary technologies
Students analyze real medical data using analytical tools, visualization and AI algorithms.
Adaptation to new situations
The content focuses on evolving technologies (AI, VR/AR, wearables) that require adaptability.
Decision-making
The study of clinical decision support systems and scenario analysis cultivates decision-making skills.
Autonomous work
Individual assignments and practical exercises enhance self-study and responsibility.
Teamwork
Students collaborate in group presentations and case studies.
Working in an international environment
Understanding international standards (e.g. HL7, DICOM) and studying global AI applications enhances international awareness.
Working in an interdisciplinary environment
The curriculum requires understanding and synthesis of knowledge from Medicine, Informatics, Bioethics and Mathematics.
Production of new research ideas
Innovative AI applications are discussed and examined, and proposals for new health solutions are created.
Project planning and management
Students manage projects and case studies from conception to presentation, acquiring design and organizational skills.
Respect for diversity and multiculturalism
Discussion about unequal access to health technologies and the need for cultural understanding.
Respect for the natural environment
Reference to environmental impacts of technology (e.g. energy use, disposal of equipment).
Demonstrating social, professional and ethical responsibility and sensitivity to gender issues
Emphasis on health data ethics and responsibility in designing and applying algorithms.
Exercising criticism and self-criticism
Critical analysis of ethical dilemmas and technological limitations promotes self-reflection.
Promotion of free, creative and inductive thinking
Engagement with interactive technologies and complex problems encourages creative and synthetic thinking.
English 
Greek